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Abstract:

Proposed is a system and method for forecasting frequencies associated to
future loss and loss distributions for individual risks of a plurality of
operating units (30) with at least one measurable liability exposure (31)
by means of independently operated liability risk drivers (311-313), and
for related automated operation of a loss resolving unit (40) by means of
a control unit controller (10). In case of an occurring loss at a loss
unit (20, . . . , 26) measure parameters are measured and transmitted to
the control unit controller (10). The control unit controller (10)
dynamically assigned the measure parameters to the liability risk drivers
(311-313) and tunes the operation of the loss resolving unit (40) by
resolving the occurred loss by means of the loss resolving unit (40).

Claims:

1. A method for forecasting frequencies associated to future loss and
loss distributions for individual risks of a plurality of operating units
(30) with at least one measurable liability exposure (31) by means of
independently operated liability risk drivers (311-313), and for related
automated operation of a loss resolving unit (40) by means of a control
unit controller (10), whereas in case of an occurring loss at a loss unit
(20, . . . , 26) measure parameters are measured and transmitted to the
control unit controller (10) and dynamically assigned to the liability
risk drivers (311-313) and whereas the operation of the loss resolving
unit (40) is automated tuned by means of the control unit controller (10)
resolving the occurred loss by means of the loss resolving unit (40),
characterized in that measuring devices (201, . . . , 261) assigned to
the loss units (20, . . . , 26) dynamically scan for measure parameters
and measurable measure parameters capturing a process dynamic and/or
static characteristic of at least one liability risk driver (311-313) are
selected by means of the control unit controller (10), in that a set (16)
of liability risk drivers (311-313) is selected by means of a driver
selector (15) of the control unit controller (10) parameterizing the
liability exposure (31) of the operating unit (30), whereas a liability
exposure signal of the operating unit (30) is generated by means of the
control unit controller (10) based upon measuring the selected measure
parameters by means of the measuring devices (201, . . . , 261), and in
that the driver selector (15) adapts dynamically the set (16) of
liability risk drivers (311-313) varying the liability risk drivers
(311-313) in relation to the measured liability exposure signal by
periodic time response, and the liability risk driven interaction between
the loss resolving unit (40) and the operating unit (30) is adjusted
based upon the adapted liability exposure signal.

2. Method according to claim 1, characterized in that measure parameters
of at least one of the liability risk drivers (311-313) of the set (16)
are generated based on saved historic data of a data storage (17), if one
or more measure parameters are not scannable for a liability risk driver
of the operating unit (30) by means of the control unit controller (10).

3. Method according to one of the claim 1 or 2, characterized in that
historic exposure and loss data assigned to a geographic region are
selected from a dedicated data storage (18) comprising region-specific
data, and historic measure parameters are generated corresponding to the
selected measure parameters and whereas the generated liability exposure
signal is weighted by means of the historic measure parameters.

4. Method according to one of claims 1 to 3, characterized in that the
measuring devices (201, . . . , 261) comprise a trigger module triggering
variation of the measure parameters and transmitting detected variations
of one or more measure parameters to the control unit controller (10).

5. Method according to one of claims 1 to 4, characterized in that the
control unit controller (10) transmits periodically a request for measure
parameter update to the measuring devices (201, . . . , 261) to detect
dynamically variations of the measure parameters.

6. Method according to one of claims 1 to 5, characterized in that if the
loss resolving unit (40) is activated by the control unit controller
(10), the loss resolving unit (40) unlocks an automated repair node
assigned to the loss resolving unit (40) by means of appropriate signal
generation and transmission to resolve the loss of the loss unit (20, . .
. , 26).

7. A system for forecasting frequencies associated to future loss and
loss distributions for individual risks of a plurality of operating units
(30) with at least one measurable liability exposure (31) by means of
independently operated liability risk drivers (311-313), and for related
automated operation of a loss resolving unit (40) by means of a control
unit controller (10), whereas in case of an occurring loss at a loss unit
(20, . . . , 26) the system comprises measuring devices (201, . . . ,
261) to scan for, measure and transmit measure parameters to the control
unit controller (10), whereas the control unit controller (10) comprises
means to dynamically assign the measured measure parameters to the
liability risk drivers (311-313) and whereas the control unit controller
(10) comprises means to operate the automated loss resolving unit (40)
resolving the occurred loss, characterized in that the control unit
controller (10) comprises a trigger module to scan measuring devices
(201, . . . , 261) assigned to the loss units (20, . . . , 26) for
measure parameters and to select measurable measure parameters capturing
a process dynamic and/or static characteristic of at least one liability
risk driver (311-313) by means of the control unit controller (10), in
that the control unit controller (10) comprises a driver selector (15) to
select a set (16) of liability risk drivers (311-313) parameterizing the
liability exposure (31) of the operating unit (30), whereas a liability
exposure signal of the operating unit (30) is generated based upon
measuring the selected measure parameters by means of the measuring
devices (201, . . . , 261), and in that the driver selector (15)
comprises means to dynamically adapt the set (16) of liability risk
drivers (311-313) varying the liability risk drivers (311-313) in
relation to the measured liability exposure signal by periodic time
response, and adjust the liability risk driven interaction between the
loss resolving unit (40) and the operating unit (30) based upon the
adapted liability exposure signal.

8. System according to claim 7, characterized in that the control unit
controller (10) comprises a switch unit to generate measure parameters of
at least one of the liability risk drivers (311-313) of the set (16)
based on saved historic data of a data storage (17), if one or more
measure parameters are not scannable for a liability risk driver of the
operating unit (30) by means of the control unit controller (10).

9. System according to one of claim 7 or 8, characterized in that a
dedicated data storage (18) of the control unit controller (10) comprises
region-specific historic exposure and loss data assigned to a geographic
region, and the control unit controller (10) comprises additional means
to generate historic measure parameters corresponding to the selected
measure parameters and to weight the generated liability exposure signal
by means of the historic measure parameters.

10. System according to one of claims 7 to 9, characterized in that the
measuring devices (201, . . . , 261) comprise a trigger module to trigger
variation of the measure parameters and to transmit detected variations
of one or more measure parameters to the control unit controller (10).

11. System according to one of claims 7 to 10, characterized in that the
control unit controller (10) comprises an interface module (14) to
transmit periodically a request for measure parameter update to the
measuring devices (201, . . . , 261) in order to detect dynamically
variations of the measure parameters.

12. System according to one of claims 7 to 11, characterized in that if
the loss resolving unit (40) is activated by the control unit controller
(10), the loss resolving unit (40) comprises a switch unit to unlock an
automated repair node assigned to the loss resolving unit (40) by means
of appropriate signal generation and transmission to resolve the loss of
the loss unit (20, . . . , 26).

Description:

FIELD OF THE INVENTION

[0001] This present invention relates to systems for forecasting
frequencies associated to future loss and loss distributions for
individual risks of a plurality of operating units with at least one
measurable liability exposure, and for related automated operating of
loss resolving units by means of an appropriate control unit controller.
Generally, the present invention relates to risk management, and more
specifically also to the field of liability risk driven exposures of
insured objects. Moreover, this invention relates to systems and methods
for developing and assessing assumptions used in designing and pricing
financial products, including insurance products.

BACKGROUND OF THE INVENTION

[0002] Risk exposure for all kinds of industries occurs in a great variety
of aspects, each having their own specific characteristics and complex
behavior. The complexity of the behavior of risk exposure driven
technical processes often has its background in the interaction with
chaotic processes occurring in nature or other artificial environments.
Good examples can be found in weather forecast, earthquake and hurricane
forecast or controlling of biological processes such as e.g. related to
heart diseases or the like. Monitoring, controlling and steering of
technical devices or processes interacting with such risk exposure is one
of the main challenges of engineering in industry in the 21st century.
Dependent or educed systems or processes from products exposed to risks
such as e.g. automated pricing tools in insurance technology or forecast
systems for natural perils or stock markets, etc. are naturally connected
to the same technical problems. Pricing insurance products is
additionally difficult because the pricing must be done before the
product is sold, but must reflect results that will not be known for some
time after the product has been bought and paid for. With tangible
products, "the cost of goods sold" is known before the product is sold
because the product is developed from raw materials which were acquired
before the product was developed. With insurance products, this is not
the case. The price of the coverage is set and all those who buy the
coverage pay the premium dollars. Subsequently, claims are paid to the
unfortunate few who experience a loss. If the amount of claims paid is
greater than the amount of premium dollars collected, then the insurance
system will make less than their expected profit and may possibly lose
money. If the insurance system has been able to predict the amount of
claims to be paid and has collected the right amount of premiums, then
the system will be profitable.

[0003] The price of an insurance product is triggered by the exposure of
the insured objects to a specific risk or peril and normally by a set of
assumptions related to expected losses, expenses, investments, etc.
Generally, the largest amount of money paid out by an insurance system is
in the payment of claims for loss. Since the actual amounts will not be
known until the future, the insurance system must rely on assumptions
about what the losses for which exposure will be. If the actual claims
payments are less than or equal to the predicted claims payments, then
the product will be profitable. If the actual claims are greater than the
predicted claims in the assumptions set in pricing, then the product will
not be profitable and the insurance system will lose money. Hence, the
ability to set assumptions for the expected losses is critical to the
success of the product. The present invention was developed to optimize
triggering of liability risk driven exposures in the insurance system
technology and to give the technical basics to provide a fully automated
pricing device for liability exposure comprising self-adapting and
self-optimizing means based upon varying liability risk drivers.

[0004] An insurance system must comprise a set of assumptions which
reflect the probabilities of occurrence of the loss being insured, the
probability of the number of people who will lapse the coverage (that is,
stop paying their premiums), and other financial elements such as future
developments in expenses, interest rates and taxes. Insurance systems can
use historical data on losses to help them to predict what future losses
will be. Professionals with experience in mathematics and statistics
called actuaries develop tables of losses that incorporate the rate of
loss for the group over time into cumulative loss rates. These tables of
cumulative loss rates can be used as one of the bases for pricing
insurance products.

[0005] In pricing a specific product, the system may start with the basic
loss tables. Then, based upon judgments concerning the specific nature of
the table, the risk to which it is applied, the design of the product,
the risk selection techniques applied at the time the policy is issued,
and other factors, the insurance system can comprise a set of assumptions
for the cumulative loss rates to serve as the foundation for the expected
future claims of the product and its risk exposures, respectively.
Depending upon the specific insurance product being developed, the
historical data and the loss tables do not always correlate well with the
specific risks which the policy has to cover. For example, most
historical data and/or insurance tables deal with the average probability
of loss in an insured set of insured objects. However, some insurance
products are directed to subgroups in a set. For example, exposure may
drastically vary in these subgroups. For example, insured objects in an
urban environment may not show the same liability exposure as such
objects in a rural environment, i.e. may be region-dependent. In order to
price products for such insured objects, insurance systems must be able
to segment the cumulative loss rate from the standard loss tables into
cohorts to tease out the loss of those who are objectively less risk
exposed within the standard group, and to tune assumptions on these more
specific subsets of the population. Segmenting these cumulative loss
rates requires that the insurance system has somehow to be able to
trigger risk factors for loss which characterize the general insured set
of insured objects versus the risk factors which signal the subset with
preferred loss. However, most historic data and/or standard loss tables
do not take into consideration such separate risk factors. The insurance
systems must trigger other sources of data to determine loss rates of
specific subsets of insurance objects and/or conditions and the risk
factors which are correlated with them. Then, in the process of pricing a
product which differentiates price based upon the risk factors, the
insurance system must set assumptions as to how these risk factors
correlate with the cumulative loss rates in the loss table. Therefore,
designing and pricing an insurance product is often an adaptive process
which is difficult to achieve by technical means. To arrive at the
overall exposure, the insurance system must be able to trigger the
appropriate assumptions of loss in which there may be multiple risk
factors, each one, individually or in combination with other factors,
derived from different simulations, historical data and loss tables.

SUMMARY OF THE INVENTION

[0006] It is an object of the invention to provide a liability risk driven
system for automated optimization and adaption in signaling generation by
triggering risk exposure of insurance objects. In particular, it is an
object of the present invention to provide a system which is better able
to capture the external and/or internal factors that affect casualty
exposure, while keeping the used trigger techniques transparent.
Moreover, the system should be better able to capture how and where risk
is transferred, which will create a more efficient and correct use of
risk and loss drivers in liability insurance technology systems.
Furthermore, it is an object of the invention to provide an adaptive
pricing tool for insurance products based upon liability exposure,
especially for mid-size risks. However, the system is not limited to
mid-size risks, but can be easily applied also to small- or large-size
risks. It is an object of the invention to develop automatable,
alternative approaches for the recognition and evaluation of liability
exposure for small- to mid-size facultative risks and in its extension
also to large-size risks. These approaches differ from traditional ones
in that they rely on underwriting experts to hypothesize the most
important characteristics and key factors from the operating environment
that impact liability exposure. The system should be self-adapting and
refining over time by utilizing data as granular statistical data
available in specific markets or from cedent's databases.

[0007] According to the present invention, these objects are achieved
particularly through the features of the independent claims. In addition,
further advantageous embodiments follow from the dependent claims and the
description.

[0008] According to the present invention, the abovementioned objects are
particularly achieved by forecasting frequencies associated to future
loss and loss distributions for individual risks of a plurality of
operating units with at least one measurable liability exposure by means
of independently operated liability risk drivers, and related automated
operating of a loss resolving unit by means of a control unit controller,
whereas in case of an occurring loss at a loss unit measure parameters
are measured and transmitted to the control unit controller and
dynamically assigned to the liability risk drivers and whereas the
operation of the loss resolving unit is automated tuned by means of the
control unit controller resolving the loss by means of the loss resolving
unit; whereas measuring devices assigned to the loss units dynamically
scan for measure parameters and measurable measure parameters capturing a
process dynamic and/or static characteristic of at least one liability
risk driver are selected by means of the control unit controller, whereas
a set of liability risk drivers is selected by means of a driver selector
of the control unit controller parameterizing the liability exposure of
the operating unit, whereas a liability exposure signal of the operating
unit is generated by means of the control unit controller based upon
measuring the selected measure parameters by means of the measuring
devices; and whereas the driver selector adapts dynamically the set of
liability risk drivers varying the liability risk drivers in relation to
the measured liability exposure signal by periodic time response, and the
liability risk driven interaction between the loss resolving unit and the
operating unit is adjusted based upon the adapted liability exposure
signal. As variant, the control unit controller steering liability risk
driven interaction between an automated loss resolving unit and a
plurality of operating units with at least one measurable liability
exposure, in case of an occurring loss at a loss unit induced by an
operating unit is activating the loss resolving unit and the loss is
automatically resolved by means of the loss resolving unit, whereas
measure parameters associated with the liability risk drivers are
measured and transmitted to a central processing device of the control
unit controller and whereas the operational interaction is adapted by
means of the central processing device, in that measuring devices
assigned to the loss units are scanned for measure parameters and
measurable measure parameters capturing a process dynamic and/or static
characteristic of at least one liability risk driver are selected by
means of the control unit controller, in that a set of liability risk
drivers is selected by means of a driver selector of the control unit
controller parameterizing the liability exposure of the operating unit,
whereas a liability exposure signal of the operating unit is generated by
means of the control unit controller based upon measuring the selected
measure parameters by means of the measuring devices, and in that the
driver selector adapts dynamically the set of liability risk drivers
varying the liability risk drivers in relation to the measured liability
exposure signal by periodic time response, and the liability risk driven
interaction between the loss resolving unit and the operating unit is
adjusted based upon the adapted liability exposure signal. A loss unit
can be any kind of device, system or even human being which is exposed to
action or interaction by the operating unit, i.e. which is exposed to the
risk of being inflicted by a matter of liability by the operating unit.
The invention has inter alia the advantage that the control system
realized as a dynamic adaptable insurance system can be fully
automatically optimized without any other technical or human
intervention. In that way, the liability risk driven system automatically
optimizes and adapts signaling generation by triggering risk exposure of
insurance objects. In particular, the invention has the advantage of
being able to capture in a better way the external and/or internal
factors that affect casualty exposure, while keeping the used trigger
techniques transparent. Moreover, the system is able to dynamically
capture and adapt how and where risk is transferred, which will create a
more efficient and correct use of risk and loss drivers in the liability
insurance technology systems. Furthermore, the invention is able to
provide an electronically automated, adaptive pricing tool for insurance
products based upon liability exposure, especially for mid-size risks.

[0009] In one embodiment variant, measure parameters of at least one of
the liability risk drivers of the set are generated based on saved
historic data of a data storage, if the measure parameter is not
scannable for the operating unit by means of the control unit controller.
This embodiment variant has inter alia the advantage that measure
parameters which are not scannable or measurable can be accounted for the
automated optimization. As a further embodiment variant, the system can
comprise a switching module comparing the exposure based upon the
liability risk drivers to the effective occurring or measured exposure by
switching automatically to liability risk drivers based on saved historic
data to minimize a possibly measured deviation of the exposures by
dynamically adapting the liability risk drivers based on saved historic
data.

[0010] In a further embodiment variant, historic exposure and loss data
assigned to a geographic region are selected from a dedicated data
storage comprising region-specific data, and historic measure parameters
are generated corresponding to the selected measure parameters and
whereas the generated liability exposure signal is weighted by means of
the historic measure parameters. This embodiment variant has inter alia
the advantage that the measure parameters and/or liability risk drivers
can automatically be weighted in relation to an understood sample of
measure data. This embodiment variant allows a further self-adaption of
the system.

[0011] In another embodiment variant, the measuring devices comprise a
trigger module triggering variation of the measure parameters and
transmitting detected variations of one or more measure parameters to the
control unit controller. This embodiment variant has inter alia the
advantage that the system automatically adapts its operation due to
occurring changes of measure parameters.

[0012] As a further embodiment variant, the control unit controller
transmits periodically a request for measure parameter update to the
measuring devices to detect dynamically variations of the measure
parameters. This embodiment variant has inter alia the same advantage as
the preceding ones.

[0013] In another embodiment variant, the loss resolving unit unlocks an
automated repair node assigned to the loss resolving unit by means of
appropriate signal generation and transmission to resolve the loss of the
loss unit, if the loss resolving unit is activated by the control unit
controller. This embodiment variant has inter alia the advantage that any
liability exposure of an operational unit can be fully automatically
handled without any interaction by an operator or the like. Furthermore,
the embodiment variant has the advantage that also decentralized located
urgent repair nodes with a variety of repair flows for dedicated
operating units can be fully automatically operated by the system.

[0014] In addition to a system, as described above, and a corresponding
method, the present invention also relates to a computer program product
including computer program code means for controlling one or more
processors of a computer system such that the computer system performs
the proposed method, in particular, a computer program product including
a computer-readable medium containing therein the computer program code
means.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention will be explained in more detail, by way of
example, with reference to the drawings in which:

[0016] FIG. 1 shows a block diagram illustrating schematically an
exemplary system for forecasting frequencies associated to future loss
and loss distributions for individual risks of a plurality of operating
units 30 with at least one measurable liability exposure 31 by means of
independently operated liability risk drivers 311-313, and for related
automated operation of a loss resolving unit 40 by means of a control
unit controller 10, whereas in case of an occurring loss at a loss unit
20, . . . , 26 measure parameters are measured and transmitted to the
control unit controller 10 and dynamically assigned to the liability risk
drivers 311-313 and whereas the operation of the loss resolving unit 40
is automated tuned by means of the control unit controller 10 resolving
the occurred loss by means of the loss resolving unit 40.

[0017] FIG. 2 shows a block diagram illustrating schematically an
exemplary recognition of risk drivers and clustering of risk drivers.
Clusters can be prioritized by the system and a first quantification of
the impact of the risk drivers is performed based on their detected loss
frequency and severity.

[0018] FIG. 3 shows a block diagram illustrating schematically the
relationship between exposure and loss. In an exemplary allocation of
risk drivers by the system, selected risk drivers are allocated to
functional modules. The indicator quantification of the selected risk
drivers is achieved by tracing measurable quantities representing the
risk driver and detecting how to measure or estimate the quantity.
Additionally an influence quantification of the selected risk drivers can
be achieved by determining model parameters for example from market
values and fitting remaining parameters (without measurable quantities
representing the risk driver) to historic exposure and loss data.

[0019] FIG. 4 shows a block diagram illustrating schematically an
exemplary dynamic expansion and further calibration of the used risk
drivers by means of the system, whereas the used set of risk drivers
mirror the structure of the outside world. The loss history is used to
further calibrate the model parameters by means of the system. Starting
from a simple set, the system gradually extends it.

[0020] FIG. 5 shows a block diagram illustrating schematically an
exemplary operation performed by means of the modules of the system. Each
loss model is based on a scenario and has one frequency and several
severity components. The linking between cause and effect and
decomposition of the risk into components is performed by means of the
system by identifying and triggering the perils (cause of potential
loss), the risk objects or activities (cause of potential loss) and/or
the scenario classes (effect of potential loss). Additional
identification can be achieved based upon the affected parties (line of
business) and/or locations (country). After identification and
decomposition a frequency distribution (mean), several severity
components (mean, standard dev.) and assigned volume are characterized by
means of the system. Finally, the links are established by means of the
system between cause, effect, and the cost of a potential loss, as well
as between the risk drivers.

[0024] FIG. 9 shows a block diagram illustrating schematically an
exemplary structure of the wording filter 134. The diagram illustrates
the modules containing appropriate functional components, whereas the
modules mirror the modeled operational realization. The wording filter
134 can be broken down into three components which are the severity
determiner, the severity limiter and the timeline processor. The severity
determiner combines the scenario loss model severity components into one
overall severity distribution per scenario. The severity limiter applies
the wording limits and deductibles to the scenario loss model overall
severity distribution and modifies the severity components accordingly.
The timeline processor adjusts the scenario loss model frequency
according to the claims trigger conditions.

[0025] FIG. 10 shows a block diagram illustrating schematically an
exemplary structure of the aggregator 135. The aggregator 135 comprises
the components frequency determiner, severity determiner, Freq/Sev
Monte-Carlo simulator and the structure module of the automated loss
resolving unit or automated loss resolving unit respectively
insurance/reinsurance unit 40. The frequency determiner is responsible
for determining the Poisson parameter for each scenario. The severity
determiner is responsible for combining the loss severity components for
a scenario to produce one overall loss severity distribution for that
scenario. The Monte-Carlo Simulator component combines the
Poisson(λi) and Pareto (cj,{circumflex over
(α)}j) distributions to form a compound distribution for each
scenario. In an another embodiment variant, as illustrated by FIG. 23,
the aggregator 135 generates the expected loss by (1) using the allocated
volume of each scenario to determine the first moment of the Poisson
frequency distribution for that scenario; (2) creating log-normal
distributions from the first two moments of the severity components of
each scenario (discrete or fitted) and apply some limits and deductibles;
(3) combining the individual loss severity component distributions for
each scenario to produce an overall loss severity distribution for that
scenario; (4) aggregating the frequency and severity distributions to
calculate losses for each scenario; (5) combining the aggregate loss
distributions of each scenario to calculate one loss distribution; and
(6) applying the reinsurance structure to the total aggregated loss
distribution to produce an expected loss cost. The (re)insurance
structure component is the last component. It contains the (re)insurance
structure (limits, deductibles, etc.) according to the loss resolving
unit 40 which is applied at a scenario level and/or at an aggregate
(adding all scenarios together) level. In the technical structure of the
data selection and data generation/formula framework, the component
consists of two stages, i.e. stage 1 and stage 2. Stage 1 comprises: (i)
Each scenario has several scenario loss severity components. (ii) Each
loss severity component from the wording filter 134 is characterized by
its own severity distribution in terms of monetary amount units. This
monetary amount is the `mean` of the severity distribution. (iii) For
each component of each scenario, there is a ratio between the standard
deviation and the mean value of the loss severity component distribution.
(iv) Each loss severity component is assumed to have a log-normal
distribution which is fully determined by the mean and the standard
deviation. The log-normal was adopted at this stage because of its
mathematical tractability. Moreover, log-normal is not an unreasonable
distribution to adopt as a single component severity distribution. This
topic will be revisited in later revisions. Stage 2 comprises: (i) The
objective to combine the severity component distributions of each
scenario to one overall distribution of that scenario. (ii) In one
embodiment variant, this can be achieved stochastically, in accordance
with a Monte Carlo simulation (as illustrated below). In another
embodiment variant, the components are combined using a convolution,
implying that the components are independent from each other.

[0026] FIG. 11 shows a block diagram illustrating schematically how the
realization of the inventive system mirrors the structure of the outside
world. Therefore, the system maps a normalized picture of the outside
world.

[0027] FIG. 12 shows a block diagram illustrating schematically another
exemplary recognition of risk drivers and clustering of risk drivers
analogous to FIG. 2. Clusters are prioritized by the system and a first
quantification of the impact of the risk drivers is performed based on
their detected loss frequency and severity. The first preliminary
recognition is generated to give the impact on loss frequency and
severity of the most important traceable risk drivers for a given set of
loss types. The number of top risk drivers is set in this example to 11
by the system. This risk driver set is used in this case to start the
dynamic adaption and/or optimization.

[0028] FIG. 13 shows a diagram illustrating schematically a loss severity
distribution whereas the loss amount is shown along the x-axis versus
1--loss probability along the y-axis. For the severity distribution tail
the Pareto distribution shows a linear behavior. The loss severity
distribution can be used by the system to eliminate systematics within
the loss history and/or loss data.

[0029] FIG. 14 shows a diagram illustrating schematically the
implementation of short-term extension modules to the system allowing a
generation of the expected loss after reinsurance risk transfer. On a
longer term, for instance, the generation of the risk capital
requirements using event-set based simulations could be possible without
the need for additional parameters or a module redesign.

[0030] FIGS. 15-18 show examples according to an embodiment variant in
which any trigger can be represented by the four time elements: causation
(action committed), loss event (occurrence), knowledge (manifestation),
claims filed (claims made).

[0031] FIG. 19 shows a diagram illustrating the adding-up of the old
years, whereas the loss burden is the result of (i) the development of
the past causation and loss event years and (ii) the attenuation of the
in-force loss event year in the light of the time window set by the
knowledge and claim filed tabs.

[0032] FIG. 20 shows a diagram illustrating the future years as the result
of the development of the in-force year and the tails of the past years,
whereas again the loss burden is the result of (i) the development of the
past causation and loss event years and (ii) the attenuation of the
in-force loss event year in the light of the time window set by the
knowledge and claim filed tabs.

[0033] FIG. 21 illustrates the overview curve according to FIGS. 21 and
22. The loss burden is the result of the development of the in-force
year. The years far ahead will bring fewer claims than the nearer
ones--whereas the in-force year has not yet developed its full potential.
There is no accumulation of years. The curve has the same shape as for
claims made but with other parameters.

[0034] FIG. 22 shows the values of the parameters of the liability risk
driver 311-313 referenced below as "claims-/loss-trigger", which are
chosen by means of the control unit controller 10 as half-life time TH
and development time TD which is the time for a time element to make
it to a claim (geometrically the distance between the start and the peak
of the bell).

[0035] FIG. 23 illustrates an embodiment variant in which the aggregator
135 generates the expected loss by (1) using the allocated volume of each
scenario to determine the first moment of the Poisson frequency
distribution for that scenario; (2) creating log-normal distributions
from the first two moments of the severity components of each scenario
(discrete or fitted) and apply some limits and deductibles; (3) combining
the individual loss severity component distributions for each scenario to
produce an overall loss severity distribution for that scenario; (4)
aggregating the frequency and severity distributions to calculate losses
for each scenario; (5) combining the aggregate loss distributions of each
scenario to calculate one loss distribution; and (6) applying the
reinsurance structure to the total aggregated loss distribution to
produce an expected loss cost. The (re)insurance structure component is
the last component. It contains the (re)insurance structure (limits,
deductibles, etc.) according to the loss resolving unit 40 which is
applied at a scenario level and/or at an aggregate (adding all scenarios
together) level.

[0036] FIGS. 24 and 25 show a preferred embodiment variant of the active
and inactive risk driver Likelihood of Mass Litigation by the path
diagram.

[0037] FIGS. 26 and 27 show a preferred embodiment variant of the active
and inactive risk driver Types of Liability by the path diagram.

[0038] FIGS. 28 and 29 show a first preferred embodiment variant of the
active and inactive risk driver Liability Laws by the path diagram.

[0039] FIGS. 30 and 31 show a second preferred embodiment variant of the
active and inactive risk driver Liability Laws by the path diagram.

[0040] FIG. 32 shows a diagram illustrating the effect of the Loss
Prevention score on frequency and severity (assuming rl=0.7,
ru=1.6). The red, yellow and green curves represent the cases of
strong, medium and weak impact.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0041] FIG. 1 illustrates schematically an architecture for a possible
realization of an embodiment of the system and method for forecasting
frequencies associated to future loss and loss distributions for
individual risks of a plurality of operating units 30 with at least one
measurable liability exposure 31 by means of independently operated
liability risk drivers 311-313, and for related automated operation of a
loss resolving unit or an automated insurance unit 40 by means of a
control unit controller 10. In case of an occurring loss at a loss unit
20, . . . , 26 measure parameters are measured and transmitted to the
control unit controller 10 and dynamically assigned to the liability risk
drivers 311-313. The operation of the loss resolving unit 40 is automated
tuned by means of the control unit controller 10 resolving the occurred
loss by means of the loss resolving unit 40 respectively the automated
insurance unit 40. The system can be realized as a liability risk driven
insurance system comprising a control unit controller 10 for steering
liability risk driven interaction control system between loss resolving
units 40 and operating units 30 with at least one measurable liability
exposure 31, i.e. with operating units 30 being exposed to a risk
measurable by physical parameters or based on appropriate physical
parameters. In FIG. 1 reference numeral 10 refers to the control unit
controller. The control unit controller 10 is realized based on underling
electronic components, steering codes and interacting interface device,
as e.g. signal generation modules, or other module interacting
electronically by means of appropriate signal generation between the loss
resolving unit 40 and the plurality of operating units 30. The inventive
system, in particular in relation to the control unit controller 10 can
be realized as independently operating expert system interacting at least
between the measuring devices 201, . . . , 261, the loss unit 20-26, the
operating unit 30 and the loss resolving unit 40 by combining them to one
functional, interconnected and component-interacting system. In
particular, the expert system functionality becomes apparent by the
control unit controller 10, scanning dynamically for measure parameters
by means of the measuring devices 201, . . . , 261. New measure
possibilities at the measuring devices 201, . . . , 261 or the loss unit
20-26 are dynamically captured by the control unit controller 10 and the
appropriate liability risk driver 311-313 are generated and assigned to
the currently measured parameters by the system. The measuring devices
201, . . . , 261 can comprise all kind of physical or analytic measure
devices, in particular all kind of sensors and data capturing or data
filtering devices. The technical set-up of the measuring devices 201, . .
. , 261 at the loss unit 20-26 have not to be known in advance or
statically kept for a certain configuration of the control unit
controller 10, but in fact the measuring devices 201, . . . , 261 are
dynamically scanned and monitored by the control unit controller 10,
whereas the control unit controller self-adapts its operational
configuration of the current assigned liability risk driver 311-313. In
the same way, the structure of the currently used liability risk driver
311-313 is adapted by the control unit controller 10 by generating and
assigning the appropriate liability risk driver 311-313 based on the
currently scanned measure parameters. The loss resolving unit 40 can
comprise any kind of damage recovery modules and/or automated repair
nodes, in particular it can be realized as an automated insurance unit,
comprising the appropriate means for electronic accounting, billing and
other transactions for compensation of losses. The damage recovery
modules can also comprise monetary based damage compensation, which is
electronically assigned to loss unit 20-26 with a loss caused by an
operating unit 30. The loss resolving unit 40 can also comprise dedicated
repair nodes comprising automatic or semiautomatic systems to maintain
operation or recover loss of the loss units 20, . . . , 26 in case of
loss. It has to be mentioned that, for many technical applications in the
insurance industry, maintenance programs or liability systems are often
statutory due to security reasons or protection of the consumer, etc. The
operating units 30 can comprise all kinds of operating or field devices,
such as for example device controllers, valves, positioners, switches,
transmitters (e.g. temperature, pressure and flow rate sensors) or any
other technical devices. An automated repair node can comprise a defined
repair flow. A repair flow comprises the process flow triggered or
initiated by a liability loss of a loss unit 20-26 caused by an operating
unit 30, as described above, to repair or replace the loss, the technical
fault or malfunction. The repair flow can comprise the use of dedicated
automated repair devices, which are controlled by the loss resolving unit
40 or the operating unit 30 or the control unit controller 10. The repair
flow can also comprise financial compensation, such as e.g. a direct
technical repair or replacement of the loss becomes impossible or the use
of dedicated repair devices is not possible due to other reasons. To
cover such cases of liabilities, the repair node can also comprise means
to initiate data transmission for financial compensation. It can be
useful for the repair nodes to comprise or have access to tracking
systems of loss on the loss units 20, . . . , 26. Normally, operating
units 30 are assigned to a user or a firm or are at least representing a
user or a firm. However, each operating unit 30 has at least one
measurable risk or exposure for arising liability to a loss unit 20-26.

[0042] The control unit controller 10 can comprise one or more data
processing units, displays and other operating elements such as a
keyboard and/or a computer mouse or another pointing device. As
illustrated schematically in FIG. 1, the control unit controller 10 as
well as the operating units 30 and the loss resolving unit 40 comprise
functional modules, such as e.g. the signaling module 11 for signal
generation and transmission 111, central processing device 13, signal
transmission interface 14/32/41, driver selector 15, data storages 17/18
and/or liability risk drivers 311-313. A person skilled in the art will
understand by viewing the specification that these functional modules are
realized at least partially as hardware components. However, a person
skilled in the art will also understand that the functional modules can
be implemented at least in parts by means of dedicated software modules.

[0043] Further to FIG. 1, reference numerals 14/32/41 refer to signal
transmission interfaces, which can be connected directly or over a data
transmission network. Therefore the control unit controller 10 and/or the
operating unit 30 and/or the Loss resolving unit 40 and/or the loss units
20-26 and/or the measuring devices 201, . . . , 261 can be connected via
a network for signal transmission. The network can comprise e.g. a
telecommunication network as a wired or wireless network, e.g. the
Internet, a GSM-network (Global System for Mobile Communication), an
UMTS-network (Universal Mobile Telecommunications System) and/or a WLAN
(Wireless Local Region Network), a Public Switched Telephone Network
(PSTN) and/or dedicated point-to-point communication lines. The control
unit controller 10 and/or the operating unit 30 and/or the loss resolving
unit 40 and/or the loss units 20-26 and/or the measuring devices 201, . .
. , 261 can also comprise a plurality of interfaces to connect to the
communication network according to the transmission standard or protocol.

[0044] At least one measurable liability exposure 31 is assigned to each
of the plurality of operating units 30. Each liability exposure 31 can be
represented by means of a liability risk driver 311-313. In FIG. 1, the
reference numeral 31 depicts the liability exposure of the "real world",
while the reference numeral 31' stands for the liability exposure based
on the risk drivers 311-313 generated by the control unit controller 10.
The liability risk drivers 311-313 are hardware and/or software based
functional modules interacting electronically with the signal generation
of the control unit controller 10. The control unit controller 10
comprises means to activate the loss resolving unit 40 in case of an
occurring loss at a loss unit 20, . . . , 26 induced by an operating unit
30 and the loss resolving unit 40 comprises automated damage recover
means to resolve the loss. Measure parameters associated with the
liability risk drivers 311-313 are measured and transmitted to a central
processing device 13 of the control unit controller 10 and the
operational interaction is adapted by means of the central processing
device 13. The control unit controller 10 comprises a trigger module to
scan measuring devices 201, . . . , 261 assigned to the loss units 20, .
. . , 26 for measure parameters and to select measurable measure
parameters capturing or partly capturing a process dynamic and/or static
characteristic of at least one liability risk driver 311-313 by means of
the control unit controller 10.

[0045] FIG. 3 shows schematically the relationship between exposure and
expected loss. FIG. 4 further shows a diagram illustrating an exemplary
allocation of risk drivers by the system and the driver selector 15.
Selected risk drivers are allocated to functional modules. The indicator
quantification of the selected risk drivers is achieved by tracing
measurable quantities representing the risk driver and detecting how to
measure or estimate the quantity. Additionally, an influence
quantification of the selected risk drivers can be achieved by
determining model parameters for example from market values and fitting
remaining parameters (without measurable quantities representing the risk
driver) to historic exposure and loss data. Thus, for operation, the
system requires a number of parameters. Some measure parameters which
cannot be measured directly, such as cost of living in various countries,
can be obtained from other sources. Other parameters such as the base
severity of a scenario class can only be obtained by comparing model
predictions with past loss experience. As FIG. 4 shows, at least one loss
experience has to be compared with a system prediction for each parameter
not obtained from other sources (risk driving properties of the real
world). To achieve this, loss experience can be split e.g. by location
(country) and/or underlying risk (risk object/activity and peril) and/or
loss components. Module based extensions to the system can e.g. allow a
generation of the expected loss after insurance risk transfer. Starting
from the simple system structure, such modular extensions are easy to
implement. The determination of the risk capital requirements using
event-set based simulations is possible without the need for additional
parameters.

[0046] For the technical realization of the system the functional units of
the control unit controller 10 can be broken down into manageable
modules, as FIG. 5 shows. In this embodiment variant, the system
comprises a scenario generator 131, a price tag engine 132, a modulation
engine 133, a plurality of loss scenarios (loss models), a wording filter
134 and an aggregator (see FIG. 5). The operation of the modules can for
example be chained to reflect the sequence (1) cause of a potential loss,
effect of the potential loss (scenario generator 131), (2) cost of the
effect of a potential loss (price tag engine 132), (3) influence of
various factors on the loss cost (modulation engine 133), (4) insurance
coverage of the potential loss (wording filter 134), (5) total expected
loss (aggregator 135). Thus, this substructure would contain five
modules, which are assigned to each other by a loss scenario
representation by means of the control unit controller 10. Each of the 5
components accommodates a number of risk drivers 311-313 and takes the
input information from the loss scenario and the exposure. The loss
scenarios are modified and passed to the next module. Through this
structure, each module is assigned to a specific set of questions.
According to FIG. 5, the scenario generator 131 can be assigned to the
conceptual objects: (i) What are the causes of a potential loss?, (ii)
What are the effects of the potential loss? and (iii) Who is affected?;
the price tag engine 132 to (i) How much does the consequence of the
potential loss cost? (ii) What is the exposure to the consequence of the
potential loss?; the modulation engine 133 to (i) Which factors related
to the insured influence a loss frequency or severity? (ii) Which factors
related to the economic, legal or societal, environment influence a loss
frequency or severity?; the wording filter 134 to (i) Which part of the
loss is covered by the insurance?; and the aggregator 135 to (ii) What is
the total expected loss corresponding to the exposure?. FIG. 6
illustrates schematically a possible structure of the scenario generator
131 in more detail.

[0047] In the inventive system, the liability risk driver structure is
based on scenarios. Loss scenarios are the system variables of the
control unit controller 10 which connect the liability risk drivers
311-313 to form a functional structure. In the following, the
relationship between the components of the control unit controller 10 of
the embodiment variant introduced above and the loss scenarios are
established. A scenario is a specific setup and flow within a series of
events or occurrences. Therefore, a scenario or the describing data and
function of the scenario comprises the answers to the questions "what
could cause a loss" and "what would be the effect of the potential loss"
with the answers to the questions "where could it happen" and "who could
be affected". Time dimensions are explicitly comprised in the control
unit controller 10. A scenario can be regarded as the entity identified
by the categories peril, risk object/activity, loss mechanism, type of
affected party, and location. The scenarios are the classes of potential
losses, and individual losses are their instances. The technical purpose
of creating scenarios is at least threefold: (1) Scenarios allow an
intuitive breakdown of a risk landscape; (2) Scenarios make it possible
to decompose the risk into components on which risk drivers act
independently; and (3) Scenarios allow the simulation of single loss sets
based on event sets, which allows an estimation of risk accumulation. A
scenario can be identified by the following categories: (i) Peril: part
of the cause of potential loss. (ii) Risk activity or risk object: part
of the cause of potential loss. (iii) Scenario class (loss mechanism):
effect of potential loss. Additionally, the following categories can be
reasonable to decompose the risk into system components of the control
unit controller 10 on which risk drivers 311-313 act independently: (iv)
Third party liability: defined by the loss resolving unit 40 line of
business (either Product Liability or Commercial General Liability), (v)
Location of potential loss: a country, in case of product liability, the
market the product is sold to, in case of commercial general liability,
the place of production. In this embodiment variant, the parameter values
"unknown" or "generic" can not only be accepted by the mentioned
components of the control unit controller 10, but can be important values
of each category. For example, there is a background scenario responsible
for all uncorrelated high-frequency/low-severity losses for each type of
affected loss units 20-26 or operating units 30. The background scenario
is identified by an unknown peril, an unknown risk activity or risk
object, an unknown mechanism, but a known type of affected party. In this
embodiment variant, the loss scenario is not normalized but rather
created out of a normalized representation in the scenario generator 131.
The subsequent financial loss is implicitly a part of each component of
the control unit controller 10, for example the financial loss according
to bodily injury. It is clear that the location of the potential loss may
differ from the location of the loss resolving unit 40, the insured, and
the permanent location of the third party based upon a specific
embodiment variant. As an example, for a specific embodiment variant, it
can be assumed that different locations for the export market, for
product liability and the place of production for commercial general
liability. Additionally, the frequency of losses may have to be generated
out of the frequency of events and the distribution of the number of
losses per event. The structure of the control unit controller 10 makes
it possible to easily incorporate such assumptions in the operation of
the system.

[0048] For each relevant scenario, there are one or several loss models.
These loss models can be called loss scenarios and are common to all the
components 131-135 of the control unit controller 10. The components
131-135 can have the following operational interaction: 1. The scenario
generator 131 (source): Based on the exposure information in the model
input, the scenario generator 131 generates scenarios. For each generated
scenario, a loss model is generated. 2. The risk drivers engines: The
risk drivers engines change the representations of these loss models or
some values thereof. 3. The aggregator 135 (destination): The destination
of the loss models is the aggregator 135 which calculates an expected
loss. The scenarios can explicitly comprise time introduced as a
dimension whereas the loss scenarios become a dependency of time t.
Very-low severity losses are frequent but neither relevant to the loss
resolving unit 40 because of a deductible or self-insured retention nor
getting reported as a consequence thereof. Therefore, a common excess
point as a monetary amount is part of all loss models. In a preferred
embodiment variant, the common excess point is 0, however there is a
credibility threshold. Since the relationship of the frequency
distribution to the exposure volume is non-linear, and the volume needs
to be split between different scenarios, different markets, etc., the
frequency distributions are volume-independent. The scenario generator
131 generates the effect of the exposure value. In one embodiment
variant, the aggregator 135 can take into account the actual exposure for
each scenario.

[0049] Furthermore, each loss scenario and therefore each loss model
normally has exactly one frequency distribution function assigned. As
taken into account by scenario generator 131, several losses may be
caused by the same event. The events are independent (dependencies can be
explicitly comprised in the control unit controller 10 using a feedback
loop between the risk driver engines). Therefore, the loss scenario
frequency distribution is a Poisson distribution characterized by the
first moment. The indictors of all external risk drivers depend on time.
However, their values are all selected according to the anticipated
in-force period of the contract parameter to be rated by the system. This
corresponds to a pure accident-year-based trending. In another embodiment
variant, the system is intended for long-tail lines of business, the
structure of the liability risk driver system can be designed with
explicit treatment of the temporal development of losses in mind. The
temporal development is split into three phases: the scenario development
depending on the characteristics of the potential losses, the claim
development depending on the characteristics of the operating environment
of the potential losses, and finally the payout process depending on the
characteristics of the potential claimants and their operating
environment. As another embodiment variant, however, the frequency
distribution can relate to a predefined reference volume throughout the
structure of the invention. Because the relationship between volume and
loss frequency cannot be assumed to be linear for the entire range of
volume, the true volume is only allocated to the different scenarios
during the aggregation into one single loss model.

[0051] where =Riλ,l=Rpipiα,j is the revenue by
product/activity/earned (in case of l equal products) or produced (in
case of/equal premises) and fikl=Fiai,kl is the frequency
of scenario ikl per unit of reference volume in industry segment i. In
this embodiment variant the parameters used are R total revenue, pi
exposure (volume) split by industry segment i, piλ;l exposure
(volume) split in industry segment i by location (country) λ for
affected party/(products or premises), Furthermore, the parameters used
are Fi base frequency, i.e. the number potential events per year and
unit of reference volume in industry segment i, ai,kl assignment
percentage of effect kl to cause i, i.e. the fraction of potential events
with effect kl in all potential events with cause and R0 reference
revenue (e.g. 100 million Euros/year). The framework in this liability
risk driver system implies a linear dependence between the company
turnover (or revenue) and the loss frequency.

[0052] In another embodiment variant of the system, the frequency
generation is based upon the fact that the observed frequency of
products- and general-liability losses is subproportional to the revenue
(turnover) and rather follows a square root with a slowly changing
prefactor:

F∝ ln2(R)R0.5,

[0053] where F is the loss frequency, and b and β are empirical
constants valid for revenues e.g. between 1 million Euros to 1 billion
Euros. To satisfy this requirement of this embodiment variant, the
frequency λiklm,λ of a potential loss associated with
the scenario ilkm (il: cause of potential loss, km: effect of potential
loss) occurring in location λ, is:

λiklm,λ=filkmφiλ;j,

[0054] where fiklm=Filail,km is the frequency of all
scenarios ilkm for one unit of LRD volume,
φiλ;l=Φpipiλ;l is the
revenue-split-dependent volume factor, Φ=aln.sup.β(Rlog
v)vb is the total volume factor (size correction for relative volume
v), and

a = 1 ln β ( R log ) ##EQU00002##

v relative volume (the liability risk driver volume V measured in
liability risk driver units), pi exposure (volume) split by industry
segment i, piλ;l exposure (volume) split in industry segment i
by location (country) λ for affected party l (products or
premises). The following further parameters used are Fil base
frequency, i.e. the number of potential events per year and unit of
reference volume in industry segment i for affected party l, ail,km
assignment percentage of effect km to cause il, i.e. the fraction of
potential events with effect km in all potential events with cause il. b
is the empirical revenue power and can be set e.g. to 0.5. β is the
empirical log power, which can be set e.g. to 2 and Rlog as log
coefficient can e.g. be set to 108.

[0055] For the generation of the relative volume v, the following
parameters are implemented: R0 as revenue constant (e.g. 100 million
Euros/year), r.sub.λ(t) relative reference revenue for location
(country) λ at time (year) t. It is important to note that despite
the different look of the generation relations in the two embodiment
variants, the frequencies of the second embodiment variant of the
liability risk driver system are equal to the frequencies generated with
the first embodiment variant using corresponding parameters, if the
company revenue parameter is equal to the reference revenue parameter,
and if the base frequencies are independent of the affected party.

[0056] Each scenario and therefore each loss model can have several loss
components. A severity distribution function characterizes the severity
of each loss component of each loss model. The split of the loss burden
into several components is essential for the separation into: (i) The
consequence of a loss (e.g. an injured person) which does not depend on
factors such as medical costs. The consequence of a loss is expressed in
natural units (e.g. number of injured persons). (ii) The cost of the
consequence of a loss (e.g. the money spent on the recovery of an injured
person) which depends on the underlying risk. Moreover, especially in the
long-tail business, the loss components have fundamentally different time
developments. By means of the additional modules of the control unit
controller 10, it can be possible to allocate the expected loss burden to
some loss components for a predefined set of concrete scenarios which
were chosen to be exemplary for a representative set of possible
scenarios leading to product liability or commercial general liability
claims. The information obtained in the manner described above is
sufficient to generate the parameters for the loss components for each
scenario. The following table gives an example of components in relation
to their natural unit and severity. However, in a preferred embodiment
variant, cost parameters can be comprised as a further component.

[0057] As an embodiment variant, the control unit controller 10 can use
such a table as a starting point. It is not and does not have to be
completed for operation, but is completed and adapted automatically by
the control unit controller 10 during operation. For example, an average
building is clearly insufficient as a natural unit since an average
building, like any other average good of a given type, is not a naturally
given unit, and the ratios between the cost e.g. of buildings, vehicles,
consumer goods and agricultural produce are not market-independent, etc.
However, the different scales prevent the components 131-135 of the
control unit controller 10 from splitting of the property damage loss
burden in terms of a count of natural units into as different types of
property such as small consumer goods and skyscrapers. This conditioning
problem can e.g. be solved by defining the property damage unit by its
cost. The effective components of property damage are added later by the
system. Any inconsistencies that arise, such as e.g. that each
subcomponent of bodily injury implicitly contains a subsequent financial
loss component whose time development is different from the time
development of the costs arising from the bodily injury directly, which
needs to be addressed by other systems separately, are overcome by the
control unit controller 10 during optimization.

[0058] In the embodiment variant, the loss component severities are
represented in different units at different places of the liability risk
drivers 311-313: (a) Natural units: After leaving the scenario generator
131, the severity of a loss given a scenario is expressed in natural
units, e.g. number of injured people. In order to facilitate
differentiation, the severity of a loss component expressed in natural
units is called a scenario loss consequence component. (b) Monetary based
units: After leaving the price tag engine 132, each loss component of
each scenario is characterized by its own severity distribution in terms
of monetary amounts. Such a severity is called herein a scenario loss
severity component. Although the overall severity often has known
properties such as a monotonically decreasing probability density
function (above a certain observation point a Pareto distribution), the
functional form of the distribution function of a single scenario loss
severity component of a single scenario is not generally known. Instead,
by means of the control unit controller 10 a scenario loss severity
component is characterized by its mean value and the standard deviation,
assuming a log-normal distribution. However, this need not strictly be
the case for all embodiment variants, since the characterization can also
be given by the mean value and the coefficient of variation rather than
the mean value and the standard deviation. In a preferred embodiment
variant, the realization is contribution dependent on the loss mechanism
and/or contribution dependent on the location. In an embodiment variant,
like the scenario loss consequence components Njα and severity
components Sjαλ, for the generation of the uncertainty
of loss severities by means of the price tag engine/determiner 132 of the
liability risk driver system the economic compensations Cjλ
for damages of type j (loss components, e.g., irreversibly injured or
dead people) at location (country) λ are characterized by their
respective mean values cjλ.sup.(1)=Cjλ
characterizing their size and the variation coefficients (ratios between
standard deviation and mean)
γjλ.sup.(2)=cjλ(2)/cjλ(1)
characterizing their relative uncertainty. However, as another embodiment
variant, the following changes can be made to improve the accuracy
especially in the prediction of the expected loss in single industry
segments where only a small number of scenarios is available: (i) The
variation coefficients of the loss consequence components
vjα(20 are no longer constants of the system but depend on the
loss component j and the loss mechanism m(α) of scenario α.
(ii) The variation coefficients of the economic compensations
γjλ.sup.(2) no longer depend only on the location
(country) λ but also on the loss component j. They take precedence
over the model-wide default γ.sup.(2). (iii) The risk driver is
realized by means of liability laws accounting for the award
predictability and increases the uncertainty accordingly. The modulator
fjαλ.sup.ra1 may or may not depend on the loss
component. As noted above, the formula for combination of the variation
coefficients depends on the distribution functions of N and C. Since they
are not known, the variation coefficients are added (based on a series
expansion around the mean values). For each loss component j of each
scenario loss model α at location (country) λ, the
uncertainty is calculated: (i) The scenario generator 131 determines the
uncertainty of the loss consequence:
vjαλ.sup.(2)=vjm(α).sup.(2), (ii) the price
tag engine 132 determines the uncertainty of the economic compensation
for one natural unit:

[0059] the price tag engine 132 combines the two uncertainties to generate
the uncertainty of the economic compensation for the potential loss:
σjαλ.sup.(2)=vjαλ.sup.(2)+γ-
jαλ.sup.(2), (iii) the modulation engine 133 increases
the uncertainty
σjαλ.sup.(2),mod=fjαλral.sig-
ma.jαλ.sup.(2) to obtain the uncertainty of the severity
of the potential loss σjαλ.sup.(2),mod. In yet
another embodiment variant, the ratios between the standard deviation and
the mean can be set as a fixed model-wide parameter. Because the
conversion between natural and monetary units occurs component-wise, a
log-normal distribution can be used in this embodiment variant both for
natural units and monetary amounts. On the other hand, any
non-multiplicative operations will make it necessary to use also other
distributions. The following table shows an exemplary loss scenario
generated by means of the control unit controller 10, which is
represented by the following components:

[0060] The table below shows another embodiment variant as an exemplary
loss scenario generated by means of the control unit controller 10. In
this embodiment variant, the loss scenario is represented by the
following components:

[0061] In the embodiment variants, the loss scenario loss is not
normalized but rather created out of a normalized representation in the
scenario generator 131. The subsequent financial loss is implicitly a
part of each component of bodily injury. The location of the potential
loss may differ from the location of the loss resolving unit 40, the
insured, and the permanent location of the third party. For the
embodiment variant, this can be assumed e.g. for the export market for
products liability and/or the place of production for commercial general
liability. It might be reasonable that the frequency of losses is
generated out of the frequency of events and the distribution of the
number of losses per event.

[0062] Exposure of information data can be one of the input parameters of
the liability risk drivers 311-313. Concerning the exemplary structure of
FIG. 5, exposure information data is used in the following components of
the control unit controller 10: (i)

[0063] Scenario generator 131: Only scenarios with corresponding exposure
are created in the scenario generator 131. (ii) Aggregator 135: The
volume splitter can be realized e.g. as a part of the aggregator 135. The
exposure can be represented by the total volume and eventual breakdowns,
which comprise: (i) Time (year), (ii) Total volume (can be monetary
amount data), (iii) Volume breakdown by underlying risk (risk
object/activity, affected party, location of potential loss), and (iv)
The risk driving properties represent the insured object and finally the
insurance wording. In some embodiment variants, it is reasonable to break
down the total exposure into components by several categories of the
underlying risk by means of a given sequence of the system. The exposure
breakdown data are usually normalized by the system. The loss units 20-26
may be qualified by a number of predefined risk driving properties.
Availability of these properties to the control unit controller 10
generally results in smaller loss frequencies and severities.
Analogously, the insurance wording may be qualified by a number of risk
driving properties. The availability of these properties also generally
results in smaller loss frequencies and severities.

[0064] According to FIG. 5, the scenario generator 131 can be assigned to
the following conceptual objects: (i) What are the causes of a potential
loss?; (ii) What are the effects of the potential loss?; (iii) Who is
affected? FIG. 6 shows schematically a possible realization of the
structure of the scenario generator 131 in more detail. The scenario
generator 131 generates loss scenarios relevant for the output by
selecting underlying risks (potential causes of loss: combinations of
peril and risk object/activity), mechanisms (potential effects of a loss)
and line of business coverage (products or commercial general liability)
and combining them into loss scenarios with the severity distribution
expressed in natural units. Scenario selection criteria of the scenario
generator 131 can comprise risk object and/or type of party affected and
line of business. The loss scenarios are represented in natural units. As
one embodiment variant, the following liability risk driver 311-313 (LRD)
identified and selected by means of the driver selector 15 can e.g. be
used in the scenario generator 131.

[0065] In this example, the insured product portfolio represents the risk
inherent to the product sold by the insured operational unit 30. The type
of product defines the type of products manufactured by the insured. As
input quantity source to the scenario generator 131, scenario base
frequencies for reference volume, reference volume and scenario base
severities can be used as input parameters. As output of the scenario
generator 131, the scenario generator 131 acts on the following on loss
model components, which are 1. Reversible/minor injury, 2.
Disability/irreversible injury, 3. Death, 4. Property damage, and 5.
Business interruption. Each underlying risk (for the time being industry
segment only) may trigger one or more scenario classes, each having its
own base severity. The scenario generator 131 further comprises a
processing module to generate the frequency of loss scenario and the
severity in natural units of the single loss components. In a preferred
embodiment variant, the measure parameters are realized in the
abovementioned liability risk driver 311-313 in that the observed
frequency of products- and general-liability losses is subproportional to
the revenue (turnover). Therefore, in a preferred embodiment variant, it
follows the square root with a slowly changing prefactor F∝
ln2(R)R0.5, where F is the loss frequency, and b and β are
empirical constants valid for revenues e.g. between 1 million Euros to 1
billion Euros. To satisfy this requirement of the liability risk system,
the frequency λilkm,λ of a potential loss associated
with the scenario ilkm (il: cause of potential loss, km: effect of
potential loss) occurring in location λ is:

λiklm,λ=filkmφiλ;l;

[0066] where filkm=Filail,km is the frequency of all
scenarios ilkm for one unit of LRD volume,
φiλ;l=Φpipiλ;l is the
revenue-split-dependent volume factor, Φ=aln.sup.β(Rlog
v)vb is the total volume factor (size correction for relative volume
v), and

a = 1 ln β ( R log ) ##EQU00004##

is a prefactor. The variables used are v relative volume (the liability
risk driver volume V measured in liability risk driver units), pi
exposure (volume) split by industry segment i, piλ;l exposure
(volume) split in industry segment i by location (country) λ for
affected party l (products or premises). The parameters used are Fil
base frequency, i.e. the number of potential events per year and unit of
reference volume in industry segment i for affected party l, ail,km
assignment percentage of effect km to cause il, i.e. the fraction of
potential events with effect km in all potential events with cause il, b
empirical revenue power (e.g. 0.5), β empirical log power (e.g. 2),
Rlog log coefficient (e.g. 108). For the generation of the
relative volume v, the following parameters used are R0 revenue
constant (e.g. 100 million Euros/year) and r.sub.λ(t) relative
reference revenue for location (country) λ, at time (year) t.

[0067] In another embodiment variant, the measure parameters are related
in the abovementioned liability risk driver 311-313 according to:

[0068] whereas Fi is the base frequency of industry segment i of loss
scenario loss ik, fik is the frequency of loss scenario ik (output),
Sk is the base severity of scenario class k, aik is the
assignment percentage of scenario class k to risk object i, pki is
the percentage of severity component j in natural units of scenario class
k, and sjk is the severity in natural units of loss component j of
scenario class k (output). FIG. 6 shows an embodiment of the scenario
selection and assembly cascade being based on the illustrated components.
In the example, the underlying risk is identified by the risk object
(type of products) and an unknown peril. Out of all possible combinations
of cause of loss i (underlying risk), effect of loss k (scenario class),
and type of party affected l (line of business), only the ones are
selected with (i) underlying risk i (ii) line of business l matching the
exposure information and (iii) scenario class k having non-zero
assignment percentages aik. The formulae for the formation of the
scenario loss consequence component mean values and the scenario
frequency mean values are given with risk driver l.

[0069] According to FIG. 5, the price tag engine 132 can be assigned to
the following conceptual objects: How much does the consequence of the
potential loss cost? and What is the exposure to the consequence of the
potential loss? It comprises conversion means for converting the severity
of the scenario loss models from natural units to monetary units by using
liability risk drivers. FIG. 7 shows schematically a possible realization
of the structure of the price tag engine 132 in more detail. The price
tag engine 132 converts the severity of the scenario loss models from
natural units to monetary units by means of using liability risk drivers
311-313. The price tag engine 132 generates the loss cost from the loss
consequence, e.g. the loss cost of injured people from the number of
injured people. Loss scenarios in natural severity units can be
transformed into loss scenarios in monetary units using market values
such as cost of living, wages, etc. The exposure (volume) is allocated to
the loss scenarios by the price tag engine 132 according to the split
over the underlying risks. Depending on exposure (volume) market split,
more than one loss scenario may be generated for one input loss scenario
by the price tag engine 132. The price tag engine 132 input and output
parameters are (a) loss scenarios as described above with input
parameters representing in natural units and output parameters
representing in monetary units; (b) exposure risk drivers included in
this module with exposure (volume) parameters by country and exposure
(volume) parameters by underlying risk. The price tag engine 132
comprises at least the functional steps of: (i) The allocation of
exposure (volume) to the different incoming loss scenarios according to
the exposure split by underlying risk. As an example embodiment, the
input parameters can be represented in natural units, whereas the output
parameters can be represented in monetary units by the price tag engine
132. (ii) If incoming loss scenarios have exposure (volume) in different
locations (e.g. countries), the price tag engine 132 creates identical
loss scenarios for each location and allocates exposure (volume)
accordingly. (iii) Determine the expected cost of each loss component of
each loss scenario. In the example, the following liability risk drivers
311-313 (LRD) are identified and selected by the driver selector 15 to be
used in the price tag engine 132.

[0071] The additional risk drivers 311-313 are combined with the cost of
living components to a total expected loss cost for each loss component
as specified with risk driver referenced as "Cost of Living". In this
case, the economic environment represents the risk related to the
economic environment in which a product is sold or manufactured. The cost
of living liability risk driver, chosen by the control unit controller 10
as an representation of economical environment, compares a basket of
non-durable and durable goods in different countries to allow
benchmarking when paying claims. The measure parameter selected by the
control unit controller 10 to measure this risk driver is a city based
index calibrated e.g. at 100 for New York containing a basket of products
corresponding to the average consumption of a European family. If a
country cannot be measured, the control unit controller 10 can e.g. use
the average of countries in the same zone. The lowest city index will be
used in the case where a country can be represented by more than one
city. As an embodiment variant, it can be assumed that the total cost
loss amount of a certain loss component a comprises measure parameters
such as e.g. pain and suffering, healthcare costs, and loss of earnings
cost components plus additional cost components related to the cost of
living risk driver. In order to establish a relationship between the cost
of living measured by appropriate measure parameters and the effective
cost components related to them, we look for factors scaling cost of
living into cost components. Since cost of living is country-specific, in
a first step it can be e.g. reasonable to assume that the scaling factors
are country-independent. In this example, for each loss component
α, the parameters can e.g. be connected based upon the following
system of relations by means of the control unit controller 10:

[0073] α=loss component (reversible/minor injury,
disability/irreversible injury, death), Cl.sup.α=total costs
for loss component α in country l (l=1, 2, . . . , n),
Cl,j=cost of the group of goods j (j=1, 2, . . . , m) in country l,
Pl=pain and suffering costs in country l, El=loss of earning
costs in country l, and Hl=healthcare costs in country l. The set of
scaling factors w.sup.α for each loss component α is
determined by solving the system of relations). Total costs
Cl.sup.α per loss component α and country l are provided
by the claims department. The costs cl,j for each group of goods j
and country l representative of the cost of living can be extracted from
appropriate data samples. Costs for pain and suffering, healthcare, and
loss of earnings per country l can be derived e.g. from data available in
the prior art. FIG. 7 shows an example of how the price tag engine 132
can be broken down e.g. into the functional components "volume allocation
matrix inducer", "market splitter", "risk object volume allocator" and
"price tag determiner". The components are interacting based upon the
measure parameters. It is clear that in order to realize the volume
allocation matrix inducer, an exposure (volume) split from the already
known information data is needed. The functional components are not
independent in the price tag engine 132. The sequence cannot simply be
altered. The market splitter needs the exposure (volume) allocated to the
incoming loss scenarios based on risk object/activity split. The price
tag determiner needs a location in order to determine the price of a loss
consequence.

[0074] For the realization of the risk object volume allocator according
to FIG. 7, the exposure (volume) is distributed over the loss scenarios
according to volume breakdown by risk object/activity. Any scenarios
sharing the risk object/activity are given the full amount allocated to
the risk object/activity. The allocation is based upon the relation:

Vik=Vpi∇Vk

[0075] whereas V is the total exposure (volume), Vik is the volume
allocated to incoming scenario ik, pj is the percentage of volume by
risk object/activity i, i is the risk object/activity, and k is the type
of affected party.

[0076] For the realization of the market splitter according to FIG. 7, the
location of each loss scenario is determined using the volume location
breakdown. If loss scenarios have exposure (volume) in different
locations (countries), identical loss scenarios for each location are
created, and the exposure (volume) is distributed accordingly. The
determination by means of the market splitter is based upon the relation:

Vikl=Vikpil

[0077] whereas Vik is the volume allocated to incoming scenario ik,
pi is the percentage of the volume allocated to risk object/activity
i by location l, Vikl is the volume allocated to outgoing scenario
ikl, and l is the location.

[0079] where Riλ,l=RPipiλ;l was the revenue by
product/activity i earned (in case of l equal products) or produced (in
case of l equal premises), fikl=Fiai,kl was the frequency
of scenario ikl per unit of reference volume in industry segment i. The
variables used are: R total revenue, pi exposure (volume) split by
industry segment i, and piλ;l exposure (volume) split in
industry segment i by location (country) λ for affected party l
(products or premises). The further parameters used are: Fi base
frequency, i.e. the number of potential events per year and unit of
reference volume in industry segment i, ai,kl assignment percentage
of effect kl to cause i, i.e. the fraction of potential events with
effect kl in all potential events with cause i, and R0 reference
revenue (e.g. 100 million Euros/year). This generation framework in the
liability risk driver system implies a linear dependence between the
company turnover (or revenue) and the loss frequency.

[0080] Note, however, that the measured and observed frequency of
products- and general-liability losses is subproportional to the revenue
(turnover). Therefore, in a preferred embodiment variant, it can be
realized to follow a square root with a slowly changing prefactor:

F∝ ln2(R)R0.5,

[0081] where F is the loss frequency, and b and β are empirical
constants valid for revenues e.g. between 1 million Euros to 1 billion
Euros. To satisfy this requirement by means of the liability risk driver
system, the frequency λiklm,λ of a potential loss
associated with the scenario iklm (il: cause of potential loss, km:
effect of potential loss) occurring in location λ is:

λiklm,λ=fiklmφiλ; l,

[0082] where filkm=Filail,km is the frequency of all
scenarios ilkm for one unit of liability risk driver volume,
φiλ;l=Φpipiλ;l is the
revenue-split-dependent volume factor, Φ=alm.sub.β(Rlog
v)vb size correction for relative volume v), and

a = 1 ln β ( R log ) ##EQU00008##

is a prefactor. The variables used are: V relative volume (the liability
risk driver volume V measured in liability risk driver units, pi
exposure (volume) split by industry segment i, and piλ;l
exposure (volume) split in industry segment i by location (country)
λ for affected party l (products or premises). The further
parameters used are: Fil base frequency, i.e. the number of
potential events per year and unit of reference volume in industry
segment i for affected party l, ail,km assignment percentage of
effect km to cause il, i.e. the fraction of potential events with effect
km in all potential events with cause il, b empirical revenue power (e.g.
0.5), β empirical log power (e.g. 2), and Rlog log coefficient
(e.g. 108). For the generation of the relative volume v, the
following parameters can be used: R0 revenue constant (e.g. 100
million Euros/year), and r.sub.λ(t) relative reference revenue for
location (country) λ at time (year) t.

[0083] For the realization of the price tag determiner according to FIG.
7, the expected cost of each loss component is determined for each
outgoing loss scenario using e.g. the abovementioned risk driver 311-313
referenced as Cost of Living. Therefore the total expected cost
Cl.sup.α of loss component α in location l is determined
using risk driver Cost of Living. It is used to convert the mean scenario
loss consequence component to a mean scenario loss severity component.
The determination by means of the price tag determiner is based upon the
relation:

sikl.sup.α=Cl.sup.αsikl.sup.α

[0084] whereas Cl.sup.α is the expected cost of α one
natural unit of loss component α in location l,
sikl.sup.α is the mean loss consequence component α of
outgoing scenario ikl (natural units), and Sikl.sup.α is the
mean loss severity component α of outgoing scenario ikl (monetary
units). Note that the above relation holds for any severity distribution
but implies the expected cost Cl.sup.α to be certain (all
moments higher than the mean are zero). As an embodiment variant, the
natural units of the property damage and financial loss components can
e.g. be tied to the natural units of the bodily injury components by the
expected loss cost. Therefore the total expected cost Cl.sup.α
of all natural property damage and financial loss components α can
be defined by (weights are unweighted average percentages of number of
affected people over all scenarios) for this example, giving e.g. a
relation:

[0085] However, since this is bound to disappear, the relation is set in a
preferred embodiment variant to

CPE,λ=0.05CDeath,λ+0.88CInjury,λ+0.07C.-
sub.Disablity,λ

[0086] The collected answers to the scenario questionnaires are kept as it
is, but before consolidating the answers, all answers given in monetary
figures are divided by the monetary amounts corresponding to the monetary
value of a defined quantity of the considered category of affected goods
in the market where the answer has been given.

[0087] According to FIG. 5, the modulation engine 133 can be assigned to
the conceptual objects (a) Which factors related to the insured influence
a loss frequency or severity?, and (b) Which factors related to the
economic, legal or societal environment influence a loss frequency or
severity? FIG. 8 shows schematically a possible realization of the
modulation engine 133 with the corresponding input and output parameters.
The modulation engine 133 is realized to alter (modulate) the loss
scenario frequency and/or severity components according to the influence
of liability risk drivers 311-313. The input and output parameters of the
modulation engine 133 are: (a) The loss scenarios. Both input and output
loss scenarios can be represented in monetary units; (b) Exposure risk
drivers comprised by the modulation engine 133 are e.g. insured
properties: turnover by employee and insured portfolio as e.g.
nanotechnology; and (c) Other risk drivers comprised by the modulation
engine 133. As mentioned, the modulation engine 133 alters the loss
scenario frequency and/or severity components according to the influence
of liability risk drivers 311-313.

[0088] The way risk drivers 311-313 influence the loss frequency or
severity in the modulation engine 133 requires the risk drivers 311-313
in the modulation engine 133 to be handled as intensive quantities. In
one embodiment variant, with increasing level of knowledge about the risk
driver 311-313 influence, some of the risk drivers 311-313 in the
modulation engine 133 might be moved to the scenario generator 131. For
example, the following liability risk drivers 311-313 (LRD) might be
selected by the driver selector 15 for use in the modulation engine 133
during operation. Note that the measure parameters traced by the system
should be measurable.

[0089] The driver selector 15 selects the risk drivers 311-313 according
to the measure parameters. In the following, the abovementioned risk
drivers 311-313 selected for the modulation engine 133 by the driver
selector 15 are discussed. The risk driver 311-313 referenced as
"frequency of class action" risk driver is assigned to whether a legal
system allows mass tort litigation through a class action system or not.
It reflects a risk environment related to the region/country. The
quantity traced and selected to measure this risk driver 311-313 is in
this embodiment example a combination of 4 (four) sub-factors, each of
which represents one aspect of the legal system in relation to class
actions. The measure parameter is region/country-specific and is the
result of a legal analysis of the four sub-factors: (1) plaintiff group
eligibility (indicates whether class actions are allowed in the country
or not), (2) recent law up-dates (indicates the trend in
legislation/litigation in the country), (3) business eligibility
(indicates whether class action litigation can apply to all areas or is
limited to certain businesses), and contingent fees (indicates whether
the lawyer remuneration system is an incentive for more class actions).
Each sub-factor can be additionally adapted to consider further needs or
attributes, e.g. set to the value 0.9 (favorable, e.g. for 10% risk
discount), 1 (neutral, no discount or loading), 1.11 (adverse, 11% risk
increase) depending on the answer to the question. This makes it possible
to achieve a balance between discounts and loadings (0.9×1.11=1
while 0.9×1.1=0.99). The sub-factor a. can e.g. be set to the power
of 3 to reflect the relative importance of this sub-factor compared to
the others. The sub-factor b. (trend) e.g. cannot be favorable when
sub-factor a. is already on favorable. The other sub-factors of the
example are independent from a. and b. and can take the three values. The
sub-factors are multiplied by one another to obtain an overall class
action factor (CAF). The control unit controller 10 always traces for
measure parameters to adapt the values and sub-factors to make them even
more objectively measurable and comparable. This is not possible with the
prior art systems. The following table shows an example of the impact
parameters of the "frequency of class action" risk driver 311-313 on loss
frequency and severity for the various loss components (legend: 3=strong
impact; 2=medium impact; 1=weak impact).

[0090] A preferred embodiment variant to the above-described example is
illustrated by the path diagram of the active risk driver Likelihood of
Mass Litigation, as given by FIG. 24. FIG. 25 shows a further path
diagram illustrating the inactive risk driver Likelihood of Mass
Litigation.

[0091] The impact on frequency and severity is simply the class action
factor magnified or diminished according to the impact table above. The
risk driver 311-313 is based upon the relation:

{tilde over (f)}i=fi(CAk).sup.χ.sup.R,A,G

{tilde over (s)}i,j=si,j(CAk)χ.sup.R,A,G

[0092] whereas CAk is the class action factor for the considered
country k, fi is the frequency of scenario loss model i, si,j
is the severity of loss component j, and χ.sub.R,A,G is the influence
exponent on the various loss components (strong, medium, weak impact).
The values for χ.sub.R,A,G are empirical values to magnify or
diminish the impact on the loss components. As an embodiment variant,
e.g. χ.sub.R,A,G=2 for strong, χ.sub.R,A,G=1 for medium, and
χ.sub.R,A,G=0.5 for weak. These values can e.g. be used for a voting
procedure. In another embodiment variant, the values can be set to
χ.sub.R,A,G=1/3 for strong, χ.sub.R,A,G=2/3 for medium, and
χ.sub.R,A,G=1.

[0093] The next risk driver 311-313 is referenced herein as "type of
liability" risk driver according to the above table. The type of this
liability risk driver 311-313 can e.g. refer to the legal mechanisms in
causation theory (strict or negligence). Strict liability means that the
claimant only needs to prove the damage and the causation to establish
liability. (S)he does not have to prove that the defendant was negligent.
The defendant in turn has limited discharge possibilities. There is often
a cap to strict liability (example: Pharmaceuticals in Germany, road
accidents, pet owners, . . . ). Negligence means that the claimant has to
prove the damage, the causation and the negligence of the plaintiff (or
his unlawfulness). The defendant is not per se liable. There is almost
never a cap to this liability (example: premises liability . . . ). In
this example, the measure parameter chosen to measure the "type of
liability" risk driver 311-313 is the percentage of the turnover realized
in business to business (B2B). This quantity may under certain
circumstances not represent accurately the strict liability/negligence
aspect. The cases identified where this matter is not the case are: (1)
retail/wholesale (in this case the products sold are all B2C but the
insured can exculpate himself on the grounds that he did not manufacture
the products himself). (2) final products sold to wholesale (in this case
the products sold are all B2B but the insured can be sued directly).
Thus, the quantity source for the input measure parameter is e.g. (a) the
"percentage of turnover" realized in business to business (B2B) retail,
or the corresponding opposite parameter "percentage of turnover" realized
in business to customer (B2C) retail. (b) Percentage of intermediaries
respectively direct recourse. Action on loss model components are the
output of this risk driver 311-313. The following table shows the impact
of the risk driver 311-313 "type of liability" on loss frequency and
severity for the various loss components (legend: 3=impact; 2=impact;
1=impact).

[0095] whereas fi is the frequency of scenario loss model i,
si,j is the severity of loss component j, d.sub.b2b is the discount
for b2b part of the business, l.sub.b2c is the loading for b2c part of
the business, b2bε[0;100%] is the turnover percentage of b2b,
b2cε[0;100%] is the turnover percentage of b2c,
drε[0;100%] is the percentage of direct recourse for b2b
business, Intε[0;100%] is the percentage of intermediaries for
b2c business, and χ.sub.R,A,G is the influence exponent on the
various loss components (strong, medium, weak impact). However, a
preferred embodiment variant to the above-described embodiment variant is
illustrated by the path diagram of the active risk driver Types of
Liability as illustrated in FIG. 26. FIG. 27 shows a further path diagram
illustrating the inactive risk driver Types of Liability.

[0096] The third selected risk driver 311-313 for the modulation engine
133 is referenced as "consumer protection laws" risk driver 311-313. As
an embodiment variant of this example risk driver, `Laws/Regulations` are
the legal grounds on which liability arises as a liability risk driver
311-313 (LRD) cluster and as opposed to the LRD cluster `Legal practice`
which is the way laws are applied in a country (i.e. the circumstances
applied in settling a claim). The liability risk driver "consumer
protection laws" represents the extent to which a legal system protects
the consumer. The mere number of consumer protection laws was considered
not to be representative of a legal system because it does not express
anything concerning the content of the law, which in turn is much more
relevant. The measure parameter chosen to measure this risk driver
311-313 is a multiplying factor per country based on specified rules. The
implemented rules make it possible to measure the values and create a
bunch of objective and measurable criteria that will be combined to
produce an adjusted quantity. As input quantity source, i.e. the source
of the selected measure parameters, class action factors are measured.
However, there are two preferred embodiment variants to the embodiment
variant above. A first preferred embodiment variant to the
above-described embodiment variant is illustrated by the path diagram of
the active risk driver Liability Laws as given in FIG. 28. FIG. 29 shows
a further path diagram of the inactive risk driver Liability Laws to this
embodiment variant. A second preferred embodiment variant to the
above-described embodiment variant is illustrated by the path diagram of
the active risk driver Liability Laws as given in FIG. 30. FIG. 31 shows
a further path diagram of the inactive risk driver Liability Laws to this
embodiment variant.

[0097] The following table shows the impact of the risk driver "consumer
protection law" 311-313 on loss frequency and severity for the various
loss components (legend: 3=strong impact; 2=medium impact; 1=weak
impact).

[0098] In the example, it can be assumed that the impact on frequency and
severity is simply the country factor magnified or diminished according
to the impact table above. The risk driver "consumer protection law"
311-313 generates the dependencies based upon the measure parameters as:

{tilde over (f)}i=fi(Lk).sup.χ.sup.R,A,G

{tilde over (s)}i,j=si,j(Lk).sup.χ.sup.R,A,G

[0099] whereas Lk is the law factor for the country k, fi is the
frequency of scenario loss model l, sij is the severity of loss
component j, and χ.sub.R,A,G is the influence exponent on the various
loss components (strong, medium, weak impact). For the measure
parameters, the values for χ.sub.R,A,G are empirical values to
magnify or diminish the impact on the loss components. χ.sub.R,A,G=2
for strong, χ.sub.R,A,G=1 for medium, χ.sub.R,A,G=0.5 for weak.

[0100] The risk driver 311-313 referenced above as "loss prevention"
defines which measures the insured has in place to reduce the frequency
and severity of his third party liability claims. The measure parameter
chosen by the driver selector 15 to measure this risk driver 311-313 is
in this example a combination of 9 (nine) sub-factors, each of which
represents one aspect of the insured's risk identification and mitigation
measures. For example, each sub-factor can have the value 0.9 (10% risk
discount), 1 (neutral), 1.11 (11% risk increase) depending on its
assessment by the underwriter. The assessment is meant to be objective in
so far as certain controls and/or processes need to be in place to
qualify for a more favorable score. The sub-factors are multiplied by one
another to obtain an overall loss prevention factor. Therefore the
overall loss prevention factor can e.g. assume values in the range from
(0.9)9=0.39 to (1.1)9=2.56 i.e. Lε[0.39,2.56]. In the
example, it is assumed that each of the nine sub-factors is equally
weighted within the basket. The input parameters of the modulation engine
133 are in this case measured regarding the following sub-factors (1)
Risk manager, (2) Business continuity management, (3) Recall plan (only
for product), (4) Certification, (5) Contract screening, (6)
Safety/Security training, (7) Complaints management, (8) Follow-up on
incidents, and (9) Environment control, audits.

[0101] Actions on loss model components are the output of the risk driver
311-313. The following table shows the impact of the risk driver "loss
prevention" 311-313 on loss frequency and severity for the various loss
components selected by the driver selector 15 (legend: 3=strong impact;
2=medium impact; 1=weak impact).

[0102] In the example given, it can be assumed that the impact on
frequency and severity is simply the prevention factor magnified or
diminished according to the impact table above. The risk driver "loss
prevention" 311-313 generates the dependencies based upon the measure
parameters as:

{tilde over (f)}i=fi(L).sup.χ.sup.R,A,G

{tilde over (s)}i,k=si,j(L).sup.χ.sub.R,A,G

[0103] whereas L is the loss prevention factor for the considered risk,
fi is the frequency of the loss scenario i, si,j is the
severity of loss component j, and χ.sub.R,A,G is the influence
exponent on the various loss components (strong, medium, weak impact).
The measure parameter values for χ.sub.R,A,G are empirical values to
magnify or diminish the impact on the loss components. As a preferred
embodiment variant, the assumptions are set so that the frequency and the
severity are simply multiplied by the prevention factor magnified or
diminished according to the impact table. The pre-processing generation
of the score is illustrated in the following embodiment example:

TABLE-US-00012
Evaluation Score Score Definition
Tier 1
Product Average 3 Score average can be reached if main certificates
Certification are in place.
Environment Good 4 Score good can be reached if all certificates (e.g.
Certification ISO 14001, EMAS or equivalent) are in place and
more than one cycle since the first certification.
Intermediary result 3
Tier 2
Business continuity Yes 1 Updated contingency planning and emergency
management response plans are in place, approved and tested
regularly.
Recall plan (only Yes 1 There is a recall plan with regular updates and
for product) trainings (e.g. mock-up recall). Products are
traceable from the moment they leave the factory
down to the final consumer.
Contract screening Yes 1 Existence of standard contracts, centralized
legal
department, regular update of standards.
Safety/Security No -1 No systematic communication on safety/security
training and no corresponding programs are implemented.
Complaints n/a 0 Complaints handling; Statistical analysis of claims
management and avoidance strategy or procedures can be
found.
Follow-up on Yes 1 All incidents, near-misses, losses, claims are
incidents investigated by root cause analysis. Prevention of
further similar cases by implementation of new
procedures, guidelines, standards and follow-up
procedures.
Intermediary result 3
Overall result 4

[0104] FIG. 32 illustrates the effect of the Loss Prevention score on
frequency and severity (assuming rl=0.7, ru=1.6). The red,
yellow and green curves represent the cases of strong, medium, and weak
impact (see Loss Prevention impact table above). The values of rl
and rr are determined from the expert estimates on the maximum
discount and loading on the expected loss as described in the
parametrization document. The details on the quantification and
generating relations can be given as Ls is the loss prevention score
for the considered risk before normalization (i.e. ε[1;4]), L is
the loss prevention factor for the considered risk (i.e.
ε[rl;ru]), CPL is the product certification score,
CGL is the environment certification score, Cn, is the
subscores (tier 2), fi is the frequency of scenario loss model l,
si,j is the severity of loss component j, l is the type of affected
party of scenario loss model l, ri is the lower limit of the
standard influence range on frequency and severity, ru is the upper
limit of the standard influence range on frequency and severity, and
χ.sub.R,A,G is the influence exponent on the various loss components
(strong, medium, weak impact). During the preconditioning phase, the loss
prevention score LS can be generated from the appropriate certification
score,

[0107] The function L is designed to have no effect on frequency and
severity if the loss prevention score equals 3, and to satisfy the
constraints on the value range, given by the parameters rl and
ru in case of χ.sub.R,A,G=1. The effect on frequency and
severity can be given by the following generation formula.

{tilde over (f)}i=fi(L).sup.χ.sup.R,A,G

{tilde over (s)}i,j=si,j(L).sup.χ.sub.R,A,G

[0108] The risk driver 311-313 referenced above as "insured
operations/human factor" reflects how much the operations are influenced
by human beings (as opposed to machines). The measure parameter chosen by
the control unit controller 10 to measure this risk driver 311-313 is the
automation factor, which can be measured as turnover by employee. This
measure parameter gives an indication of the level of automation in the
product development process of the insured. In this example, the
assumption is that average automation factors per industry are available.
With this assumption, the risk can be graded by the control unit
controller 10 depending on the industry that was chosen and on where it
is compared with its industry benchmark. As input quantity source for
this risk driver, the number of employees and turnover are properties of
the insured and are therefore selected by the system. Actions on loss
model components are the output of the risk driver 311-311. The table
below shows the impact of the risk driver "human factor" 311-313 on loss
frequency and severity for the various loss components (legend: 3=strong
impact; 2=medium impact; 1=weak impact).

[0109] When the automation factor increases with respect to the average
value for the specific industry segment, it is assumed in this embodiment
variant that the degree of automation of the insured operating unit 30 is
the same and less employees are doing the same amount of work. Therefore
the control unit controller 10 assumes an increase in errors due to human
factor and the human factor is >1. A further increase in the
automation factor implies an increase of the automation and therefore a
decrease in error due to human factor and the human factor is <1. In
the same way, when the automation factor decreases with respect to the
average value for the specific industry segment it can be assumed that
the degree of automation of the insured operating unit 30 is the same and
more employees are doing the same amount of work. Therefore, it is
assumed that there is a decrease in errors due to human factor and the
human factor is <1. A further decrease in the automation factor
implies a decrease of the automation and therefore an increase in error
due to human factor and the human factor is >1. The risk driver
"insured operations/human factor" 311-313 generates the dependencies
based upon the measure parameters as:

{tilde over (f)}i=fiHk

{tilde over (s)}i,j=si,jHk

[0110] whereas t is the automation factor, tk is the
industry-specific reference automation factor of industry segment k,
fi is the frequency of scenario loss model i, and sij is the
severity of loss component j. Further with

[0111] The relation used to quantify the human factor Hk is shown in
FIG. 12. Note that the function makes use of the two parameters d and
h.sub.R,A,G with the following meaning: d=interval of values for the
automation factor within which a discount >1 and <1 is given,
respectively for greater and smaller values of the automation factor
(i.e. in the interval [tk(1-d), tk(1+d]). h.sub.R,A,G=highest
amount of discount given within the interval [tk(1-d),tk(1+d].
We can preliminary assign h.sub.R,A,G=20%, 10%, and 5% for red, amber and
green. FIG. 12 shows a curve with tk=1, d=20%, h.sub.R,A,G=20% (i.e.
red). The three colors represent Dk(t) for the ranges 1), 2) and 3),
i.e. for low, mid and high values of t.

[0112] Finally, the risk driver 311-313 referenced above as "new
hazards/nanotechnology" represents the risk inherent to products based on
new scientific developments for which some risks might have not yet
materialized. Nanotechnology was chosen herein as an example for new
hazards and how the control unit controller 10 measures it by means of
the measure parameters. The measure parameter selected by the control
unit controller 10 to measure this risk driver 311-313 is the innovation
factor. The innovation factor can be given as investment amount divided
by turnover. The measure of the innovation factor goes beyond the measure
of the nanotechnology risk driver 311-313 per se and it is more a measure
of the new hazards cluster. Further granularity for specific hazards in
the quantification is reached during operation of the control unit
controller 10, by triggering for additional measure parameters and more
exact measuring of available measure parameters such as, e.g., investment
in nanotechnology amount divided by turnover are available. As quantity
source for the input measure parameters, the control unit controller 10
selects in this embodiment variant the investment amount and turnover as
properties of the insured operating unit 30. In the embodiment variant,
the average on all industries of the innovation factor is e.g. 4%
(expected value). In a first step the control unit controller 10 can
generate the impact on loss frequency and severity with respect to this
reference point. However, certain industries such as pharmaceuticals,
chemicals and IT invest more money in innovation. These are those with a
higher technology risk.

[0113] Therefore, in a second step, the 4% average value can be corrected
for each industry segment level k according to, e.g., a correction factor
ck. The impact on loss frequency and severity should be re-modeled
making use of the increased information at the higher degree of
granularity. In this embodiment variant, it is simply assumed that all
ck=1 for all k.

[0114] Actions on loss model components are the output of the risk driver
311-313. The table below shows the impact of the risk driver
"nanotechnology" 311-313 on loss frequency and severity for the various
loss components (legend: 3=strong impact; 2=medium impact; 1=weak
impact).

[0115] In the embodiment variant, an exponential dependency of the
frequency and of the severity on the innovation factor is assumed.
Dependency is assumed to be the same. Parameters of the exponential
function can e.g. be determined assuming no impact for values of
innovation factor <=4% and an increase of 50% in loss frequency and
severity for innovation factor=30% (the latter value is regarded as an
upper limit for the innovation factor, even if there is no limit for the
possible values that the innovation factor may assume).

[0116] The risk driver "nanotechnology" 311-313 generates the dependencies
based upon the measure parameters as:

[0117] whereas l is the innovation factor, lA is the innovation
factor's average (=0.04), fi is the frequency of scenario loss model
i, and sij is the severity of loss component j. The values for the
parameter bA,G have been determined assuming no impact for values of
innovation factor ≦4% and an increase of 50% and 25% in loss
frequency and severity for innovation factor=30%. For the embodiment
variant, it can be observed that the value of 30% of the innovation
factor is regarded as an upper limit for the innovation factor (even if
there is no limit for the possible values that the innovation factor may
assume). FIG. 14 illustrates the characteristics of the innovation
factor, as generated by the control unit controller 10. As shown in FIG.
14, the values obtained are bA,G=1.064 (amber in FIG. 14) and
bA,G=1.035 (green in FIG. 14). In FIG. 14, the multiplying factor is
equal to

b A , G ( I - I A ) I A . ##EQU00013##

[0118] According to FIG. 5, the wording filter 134 can be assigned to the
conceptual object: Which part of the loss is covered by the insurance?
FIG. 9 shows schematically a possible realization of the structure of the
wording filter 134 in more detail. The wording filter 134 filters the
elements of loss scenarios according to the wording inclusions,
exclusions and limitations. The input loss scenarios reflect the losses
as they happened, the output loss scenarios reflect the losses as they
are covered and likely to be claimed. The wording filter 134 input and
output parameters are: (a) Loss scenarios as described above. Both input
and output loss scenarios are represented in monetary units. (b)
Insurance wording risk drivers included in the wording filter 134 are the
limits and deductibles and the claims-/loss-trigger. All the
above-described components before the wording filter 134 comprise the
potential losses independently of a possible intervention by the loss
resolving unit 40, i.e. a potential cover of them. The wording filter 134
can explicitly apply the insurance conditions on the loss scenario: (a)
It adjusts the severity components according to limits and deductibles.
(b) It adjusts the frequency according to the claims trigger conditions.
(c) It also will take into account some wording exclusions in a future
version. The way risk drivers 311-313 influence the loss frequency or
severity in the wording filter 134 requires risk drivers 311-313 in the
modulation engine 133 to be considered as intensive quantities. With an
increasing level of knowledge accumulation by the system about the risk
driver 311-313 influence, some of the risk drivers 311-313 in the wording
filter 134 might be moved to the scenario generator 131.

[0119] The following liability risk drivers 311-313 are e.g. traced and
selected by the driver selector 15 for the wording filter 134 herein
referenced as (i) "claims-/loss-trigger" and (ii) "limits and
deductibles". In this example, the risk driver 311-313 referenced as
claims-/loss-trigger reflects the mechanisms according to which the time
elements of a claim are taken into account to tell whether it qualifies
to be filed under the policy. There are universal triggers used in
casualty business. These are: (i) action committed, (ii) occurrence,
(iii) manifestation, (iv) claims made. Furthermore there are buffer
dates/periods such as (i) retroactive date; (ii) sunset; (iii) extended
reporting period. These can substantially modify the scope of application
of the policy, which can be considered in this system as additional
parameters.

[0120] However, in the wording filter 134, the terminology used is not
limited to these triggers and/or may refer to partial elements of the
trigger. This is due not only to language inaccuracy but also to the fact
that wordings can be subject to interpretation. A simple example is the
case of the French `Loi sur la securite financiere` that is often
referred to as `French claims made`. In fact the time element referred to
in the unlimited retroactive period is meant to be `occurrence` but the
French word `fait dommageable` actually means `causation`. Strictly
speaking this trigger is not equivalent to a `claims made` for which the
retroactive date normally refers to the occurrence. Thus the wording
filter 134 must be able to scope with such interpretational problems. For
the present embodiment variant of the control unit controller 10, it is
assumed that any claim trigger at large (i.e. including all time buffer)
can be accurately represented through a combination of several time
windows in which specific claims characteristics have to fall in order to
qualify for the claim to be filed under the policy. For example an
occurrence claims made trigger with 2 years sunset clause can be
represented by a loss event time window and a claim filed window. Each
window can be defined by two tabulators: (a) the entry tabulator (in-tab)
that is the earliest date after which the characteristic has to take
place; (b) the exit tabulator (out-tab) that is the latest date by which
the characteristic has to take place. As an embodiment variant, any
trigger can e.g. be represented by the four time elements: causation
(action committed), loss event (occurrence), knowledge (manifestation),
claims filed (claims made). FIGS. 17-20 show examples according to this
embodiment variant. Based on the above graphs, the shape of the loss
burden curve is determined. On the same principle that any trigger can be
depicted by the position of the four a.m. tabs, it is determined whether
the loss burden curve has the same shape regardless of the trigger. The
loss burden for the whole time bar is the sum of all potential claims to
happen, whether these qualify to be filed under the policy or not. The
loss burden for a policy is the sum of all potential claims to happen and
be legitimately filed under the policy. Using the time tabulators is like
cutting off the tails of the loss burden for the whole time bar. If it is
assumed that the time elements of a claim are independent we can say that
the loss burden for a policy is the sum of all potential from the
different time elements. While all elements may lie in the considered
year, the past exposure is only represented by the causation and the
occurrence elements as only these can lie in the past before it comes to
a claim under the policy. Similarly the future exposure is only
represented by the manifestation and the claim filed elements as only
these can lie in the future.

[0121] The loss burden is the result of (1) the development of the past
causation and loss event years. The oldest years bring fewer claims than
the youngest ones--whereas very young years have not yet developed their
full potential; (2) the attenuation of the in-force loss event year (no
exposure for the years afterwards as the expiry cuts off loss events) in
the light of the time window set by the knowledge and claim filed tabs.
The old years can be depicted/added up as shown in FIG. 21. The future
years are the result of the development of the in-force year and the
tails of the past years. It can be illustrated as shown in FIG. 22. The
overview curve can be represented as shown in FIG. 23. The loss burden is
exclusively the result of the development of the in-force year. According
to the above comment on claims made, the years far ahead will bring fewer
claims than the nearer ones--whereas the in-force year has not yet
developed its full potential. There is no accumulation of years. In other
words, the curve has the same shape as for claims made but with other
parameters.

[0122] The following properties about the curves are known thus far by the
liability risk driver 311-313 referenced herein as
"claims-/loss-trigger": (i) the area beneath the curve represents the
loss burden regardless of the triggers (i.e. the tabs) chosen; (ii) since
the loss burden is not infinite they must be decreasing asymptotically
faster than x-1; (iii) according to expert judgment an occurrence
policy (with no sunset, i.e. with no future cut-off--except statute of
limitation) bears a higher risk than a claims made policy. The curve on
the left-hand side has to diminish faster than the curve on the
right-hand side. It is self-evident that the time elements causation,
occurrence, manifestation, claim filed are subsequent. To make it
relevant to the loss resolving unit 40 a causation needs to make it to an
occurrence, an occurrence needs to make it to a manifestation and a
manifestation needs to make it to a claim. For the signal processing of
the liability risk driver 311-313 claims-/loss-trigger, as few parameters
as possible are used to fully describe the curve. The values of these
parameters are chosen by means of the control unit controller 10 as
half-life time TH and development time TD which is the time for a
time element to make it to a claim (geometrically the distance between
the start and the peak of the bell). This is illustrated by FIG. 24. As
quantity source for the input of the wording filter 134, the
claims-/loss-trigger as liability risk driver 311-313 is an input
property to the wording filter 134. For the output, the claims trigger
acts directly on the loss scenario frequency distribution. The effect on
the severity is indirect. tm is the time measured by the system
between the end of the in-force period and either the in-tab or the
out-tab (depending on the timeframe) of timeframe m. The risk driver
311-313 can e.g. be based upon:

[0123] The function F fulfils the basic requirements subject to some
constraints on TD. The functions Fm(tm) for the four
timeframes m are multiplied by the scenario loss model frequency. As
illustrated in FIG. 9, the wording filter 134 can be broken down into
three components, which are (a) the severity determiner, (b) the severity
limiter and (c) the timeline processor. The severity determiner (a)
combines the scenario loss model severity components into one overall
severity distribution per scenario. This distribution currently is a
Pareto distribution. The severity determiner works in the same way as the
corresponding component of the aggregator 135. The severity limiter (b)
applies the wording limits and deductibles to the scenario loss model
overall severity distribution and modifies the severity components
accordingly. The severity limiter cuts off the parts of the loss scenario
overall severity distribution which are not covered due to wording limits
and deductibles. It modifies the scenario loss model severity components
at the lower and the upper end of the expected severity range such that
the ratio between the sum of the resulting severity component mean values
and the incoming severity component mean values equals the ratio between
the covered and the full overall severity. The timeline processor (c)
adjusts the scenario loss model frequency according to the claims trigger
conditions. The timeline processor translates the claims trigger wording
into four (country-specific) timeframes within which the loss must have
been the time of the (i) causation (action committed), (ii) loss event
(occurred), (iii) knowledge (manifested), and (iv) claim (claim filed)
must fall for a loss to be covered by the insurance policy under
consideration.

[0124] As shown in FIG. 5, the aggregator 135 is the final operational
unit in this sequence splitting. The aggregator 135 combines several loss
scenarios to produce an expected loss. FIG. 10 shows schematically a
possible realization of the structure of the aggregator 135 in more
detail. The aggregator 135 generates the expected loss from several loss
scenarios as described above. The input signals into the aggregator 135
are the loss scenarios (output) from the wording filter 134. These loss
scenarios (output) from the wording filter 134 reflect the losses which
are covered and likely to be claimed. The aggregator 135 then combines
these loss scenarios to produce one expected loss across all scenarios.
In general, the aggregator 135 produces the expected loss by (i) using
the allocated volume of each scenario to determine the first moment of
the Poisson frequency distribution for that scenario; (ii) combining the
individual loss severity components for each scenario to produce an
overall Pareto loss severity distribution for that scenario; (iii) using
the Poisson and Pareto distributions to simulate losses for each
scenario; and (iv) applying the reinsurance structure to the simulated
losses to produce an expected loss cost.

[0125] In one embodiment variant, the driver selector 15 identified
selected the following liability risk drivers 311-313 (LRD) to be used by
the aggregator 135.

TABLE-US-00015
LRD Cluster LRD Member LRD Quantity
Insured Geographical Extension Income by Market
Characteristics of Activity
Insured Geographical Extension Wages per country
Characteristics of Activity divided by median
income

[0126] The quantity definitions of the liability risk driver 311-313
referred to as "Geographical Extension of Activity" comprises the
geographic scope of activities defining the spread of activities by
country and/or regions. The following quantities are used to characterize
the human factor: (a) Sales per country (geographic split of sales
divided by corresponding PPP): this quantity is taken as the exposure
(volume) in case of product liability. In this embodiment variant, the
risk was not captured that a product may be sold on from one country to
the other. (b) Wages per country in median income (the amount of salaries
paid in a country divided by the median income of this country): this
quantity is taken as the exposure (volume) in case of premises liability.
The median income takes out the distortion caused by costs of living in a
country. There are quantities describing the geographic extension of
activity which are modulators and can also become relevant to the
modulation engine 133. In this embodiment variant, sales per country are
used as exposure (volume) for commercial general liability as well. As
input quantity source, the exposure (volume) is used by the system. The
output generates the operation on the loss model components. By
definition, the exposure (volume) directly determines the frequency. The
technical framework on exposure (volume) allocation is given with the
price tag engine 132 defined above and the technical framework on
volume-frequency relationships is given with the aggregator 135.

[0127] According to FIG. 10, the aggregator 135 comprises the components
frequency determiner, severity determiner, Freq/Sev Monte-Carlo simulator
and the structure module of the loss resolving unit 40. The frequency
determiner and the severity determiner components operate independently
of each other. The frequency determiner is responsible for determining
the Poisson parameter for each scenario. In the following, RefVoli
refers to the reference volume for scenario i. The reference volume can
be predefined by means of the system. AllocVoli refers to the
allocated volume for scenario i. The allocated volume can be generated in
the price tag engine 132. Note that the risk drivers 311-313 described
above are not used in the aggregator 135. They are, however, used in the
price tag engine 132 in order to calculate the AllocVoli, which is
then used in the aggregator 135. εi refers to the predefined
frequency rate for scenario i based on the reference volume for scenario
i. εi is the `number of claims per unit of time per unit of
reference volume` based on the reference volume for scenario i.
λl refers to the Poisson parameter for scenario i based on the
allocated volume for scenario i. λi is related in the
aggregator 135 as

λ i = i RefVol i × AllocVol i ##EQU00015##

[0128] Therefore, λi is proportional to allocated volume.
However, it would have been just as appropriate to have assumed that
λi is proportional to some sort of function of the allocated
volume [i.e. F(AllocVoli) where F is a relation representable as a
function]. Note that in the case of a non-linear volume-frequency
relationship, frequency additively is not naturally given. In this
embodiment variant, a linear function [i.e. F(x)=x] is adopted. The
output from the frequency determiner is a Poisson distribution with
parameter λi for each scenario.

[0129] According to FIG. 10, the severity determiner is responsible for
combining the loss severity components for a scenario to produce one
overall loss severity distribution for that scenario. The severity
determiner consists of two stages--stage 1 and stage 2. For stage 1,
recall that each scenario has several scenario loss severity components.
Each loss severity component from the wording filter 134 is characterized
by its own severity distribution in terms of monetary amount data. This
monetary amount is the `mean` of the severity distribution. For each
scenario, there is a different but predefined ratio, which applies to the
`mean` of each loss severity component for that scenario. The ratio is
defined as (standard deviation/mean). In other words, multiplying the
ratio to the `mean` gives us the standard deviation for the loss severity
components. As an embodiment variant, each loss severity component is
assumed to have a log-normal distribution. Hence, given the `mean` and
the ratio, the log-normal distribution would be fully specified. However,
it would have been just as appropriate to have assumed distributions
other than log-normal. The log-normal was adopted at this stage because
of its mathematical tractability. Moreover, log-normal is not an
unreasonable distribution to adopt as a severity distribution. Let the
loss severity component for a particular scenario i be represented by
integers 1 to im, where the subscript m denotes the total number of
loss severity components in scenario i. ij refers to the jth
loss severity component of scenario i. Note that j≦im. Let
μij denote the `mean` of the loss severity component j for
scenario i (recall that each scenario can have multiple loss severity
components). ξi refers to the predefined ratio for scenario i.
Let σij, denote the standard deviation of the scenario loss
severity component j for scenario i. Then
σij=ξi×μij. At the end of stage 1,
log-normal(μij,σij) distributions are produced for
every combination of i and j. Note that m is not necessarily the same for
each scenario. For example, it is possible for scenario 1 to have 3 loss
severity components (i.e. m equals 3), but scenario 2 could have 2 loss
severity components (i.e. m equals 2).

[0130] In Stage 2, the objective of the severity determiner is to combine
the log-normal (μij,σij) so that one overall Pareto
distribution is produced for each scenario. In other words, for a given
scenario i, Stage 2's objective is to determine a Pareto distribution
that best describes the combination of log-normal (μij,
σij) for j: 1 to im. As an embodiment variant, the Pareto
can be adopted as the overall distribution for each scenario because of
its slow, monotonically decreasing tail. This is achieved through the use
of a severity simulator. Given a scenario i, the simulator simulates
losses across all loss severity components (j:1 to im) from the
log-normal (μi,j,σi,j) distributions. n refers to the
number of losses simulated for each and every loss severity component.
This means that, for each scenario, there will be nim (n times
im) total simulated losses.

[0131] The next step of the severity determiner is to fit appropriate
Pareto distributions using these simulated losses. The parameters for the
`best-fitting` Pareto distributions are derived using maximum likelihood
estimation. X1, . . . , Xnim refers to the simulated
losses from scenario i. c refers to the threshold parameter for the
Pareto distribution for scenario i. αi refers to the shape
parameter for the Pareto distribution for scenario i. It can be shown
that the maximum likelihood estimator for ci is:

c ^ i = min k X k where k = 1
ni m ##EQU00016##

[0132] The maximum likelihood estimator for αi is:

α ^ i = ni m k = 1 k = ni m X k
##EQU00017##

[0133] Hence the output from Stage 2 is a Pareto distribution with
parameters (ci, {circumflex over (α)}i) for each
scenario. The severity simulator can e.g. use seeding to ensure that
results remain consistent. However, the user can be allowed to vary or to
seed in order to test other random simulations. The user can also be
allowed to vary the number of simulations (i.e. n). The Monte-Carlo
Simulator component, as shown in FIG. 10, combines the Poisson
(λi) and Pareto (ci,αi) distributions to form
a compound distribution for each scenario. The simulator first simulates
the number of claims for scenario i from Poisson (λi). Then,
for each simulated claim, the simulator simulates the loss severity from
Pareto (ci, αi). The process is repeated for each and
every scenario. As an embodiment variant, the simulator can use seeding
to ensure that results remain consistent. However, the user can be
allowed to vary or to seed other random simulation combinations. The user
can also be allowed to vary the number of simulations. Finally, the
(re)insurance structure component, according to FIG. 10, is the last
component. It contains the (re)insurance structure (limits, deductibles,
etc.) of the loss resolving unit 40 which is applied at a scenario level
and/or at an aggregate (adding all scenarios together) level. The result
is the expected loss to the (re)insurance structure.

[0134] The control unit controller 10 needs to be calibrated. This
activity can be pursued by the system by means of severity curves at
various level of granularity which have been determined e.g. by the
liability risk drivers 311-313 for one or a plurality of pilot markets
such as e.g. Australia, Germany and Spain. As illustrated schematically
in FIG. 16, extensions to the model allowing a calculation of the
expected loss after reinsurance risk transfer are easy to implement in
the inventive system. Additionally, for instance, the calculation of the
risk capital requirements using event-set based simulations is possible
without the need for additional parameters or a model redesign. This is
not possible with the prior art systems.

[0135] FIG. 2/9 shows a diagram illustrating an exemplary recognition of
risk drivers and clustering of risk drivers. Clusters can be prioritized
by the system and a first quantification of the impact of the risk
drivers is performed based on their detected loss frequency and severity.
The example of FIG. 9 is based on a set of eleven risk drivers that were
prioritized by the driver selector 15. The system can be divided into
five functional modules. In the example of FIG. 5, the chain of modules
reflects the sequence: (i) cause of a potential loss, effect of the
potential loss (scenario generator 131); (ii) cost of the effect of a
potential loss (price tag engine 132), (iii) influence of various factors
on the loss cost (modulation engine 133); (iv) insurance coverage of the
potential loss (wording filter 134); (v) total expected loss (aggregator
135). The modules are connected by a definable scenario loss model
representation. Each module accommodates a number of risk drivers and
takes the input information from the scenario loss models and the
exposure. The scenario loss models are modified and passed to the next
module. The choice of how to measure the risk drivers and the
quantification of their impact on frequency and severity is achieved by
means of the system, as described below. Furthermore, the system needs to
be calibrated. This operation is pursued by the use of severity curves at
various level of granularity which can also be performed in a first step
restricted to exemplary region or markets.

[0136] Quantification for type of loss has to be achieved by the control
unit controller 10 or the driver selector 15. As an embodiment variant,
this can be achieved by means of the mentioned scenario generator 131
generating samples of loss scenarios. The control unit controller 10
estimates how the total loss generated by each scenario is distributed
among the various types of loss (bodily injury, property damage,
financial loss). In the next step, the selectable risk drivers are
prioritized by the control unit controller 10 or the driver selector 15.
Prioritization comprises prioritizing the clusters and identifying the
most important risk drivers within each cluster. In the next step, the
control unit controller 10 provides a first preliminary estimate of the
impact on loss frequency and severity of the most significant risk
drivers for a given set of loss types. The preliminary selection can be
based upon the value of a definable threshold value. The preliminary
selection can be used as starting set for the inventive adaption and
optimization of the system. In the example of FIG. 9, the selection
comprises eleven risk drivers.

[0137] As mentioned, the control unit controller 10 comprises a trigger
module to scan measuring devices 201, . . . , 261 assigned to the loss
units 20, . . . , 26 for measure parameters and to select measurable
measure parameters capturing or partly capturing a process dynamic and/or
static characteristic of at least one liability risk driver 311-313 by
means of the control unit controller 10. That is to say, for each risk
driver, the system selects the most representative measureable indicator.
In one embodiment variant, the system conducts self-testing based upon
cross-country or cross-risk consistency. FIG. 8 shows an example of the
triggered measurable quantity for the risk driver "human factor"
according to FIG. 2/9. The tracing of measurable quantities representing
the risk driver shows the relation of the human factor to the measure
parameter characterized by the turnover per employee. This measure
parameter turn-over per employee can be triggered or measured by the
system. The curve based on the system tracing shows that the turnover per
employee can represent whether a firm has automated processes. The curve
further indicates that a firm increasing its turnover per employee does
not immediately mean a higher degree of automation. Far more it can be
considered as putting more pressure on the employees by reducing the
staff. Therefore, the measure parameter is not unambiguous for
measurements of the human factor in this region. Only when the increase
is significant enough can the system measure unambiguously that the
process automation has been increased. As described above, FIG. 9 shows a
block diagram illustrating schematically another exemplary recognition of
risk drivers and clustering of risk drivers analogous to FIG. 2. Clusters
are prioritized by the system and a first quantification of the impact of
the risk drivers is performed based on their detected loss frequency and
severity. The first preliminary recognition is generated to give the
impact on loss frequency and severity of the most important traceable
risk drivers for a given set of loss types. The number of top risk
drivers is set in this example to 11 by the system. This risk driver set
is used in this case to start the dynamic adaption and/or optimization.

[0138] As already described above, FIG. 10 shows another example of the
triggered measurable quantity for the risk driver "nanotechnology factor"
according to FIG. 9. The risk driver is allocated by the system along
with the embedding in a module. FIG. 10 shows how the measure parameter
"innovation factor" influences severity and frequency. The innovation
factor is the measure parameter selected by the system according to its
traceable relation to the liability risk driver referenced
"nanotechnology". The measure parameter innovation factor equals
investment in research and development expressed in percentage of
turnover. The amber curve shows the relation for medium impact, while the
green curve shows the relation for weak impact. The measuring devices
201, . . . , 261 can comprise a trigger module to trigger variation of
the measure parameters and to transmit detected variations of one or more
measure parameters to the control unit controller 10. Additionally, the
control unit controller 10 can comprise an interface module 14 to
transmit periodically a request for measure parameter update to the
measuring devices 201, . . . , 261 in order to detect dynamically
variations of the measure parameters. As an embodiment variant, the
control unit controller 10 can comprise a switch unit to generate measure
parameters of at least one of the liability risk drivers 311-313 of the
set 16 based on saved historic data of a data storage 17, if one or more
measure parameters are not determinable and/or scannable for the
liability risk driver of the operating unit 30 by means of the control
unit controller 10.

[0139] The control unit controller 10 comprises a driver selector 15 to
select a set 16 of liability risk drivers 311-313 parameterizing the
liability exposure 31 of the operating unit 30. A liability exposure
signal of the operating unit 30 is generated based upon measuring the
selected measure parameters by means of the measuring devices 201, . . .
, 261. The driver selector 15 comprises means to dynamically adapt the
set 16 of liability risk drivers 311-313 varying the liability risk
drivers 311-313 in relation to the measured liability exposure signal by
periodic time response, and adjusts the liability risk driven interaction
between the loss resolving unit 40 and the operating unit 30 based upon
the adapted liability exposure signal. If the loss resolving unit 40 is
activated by the control unit controller 10, the loss resolving unit 40
can comprise a switch unit to unlock an automated repair node assigned to
the loss resolving unit 40 by means of appropriate signal generation and
transmission to resolve the loss of the loss unit 20, . . . , 26. To
weight the generated liability exposure signal, a dedicated data storage
18 of the control unit controller 10 can comprise region-specific
historic exposure and loss data assigned to a geographic region, and the
control unit controller 10 can comprise additional means to generate
historic measure parameters corresponding to the selected measure
parameters and to weight the generated liability exposure signal by means
of the historic measure parameters.

[0140] The present liability risk driven system meets the following
objectives, which cannot be achieved by the prior art systems, as known
up to now. The inventive system can explicitly take into account the
risk-driving properties of the underlying risk. All risk-driving aspects
of the legal or societal environment are explicitly and automatically
incorporated by means of the system. The system is easily adaptable to
future extensions (e.g. simulation of risk accumulation by applying event
sets). A further advantage is that only a minimum set of parameters is
required with the inventive system and, among the other advantages, the
inventive system is also able to anticipate the effect of legal or
societal changes on the expected loss by means of the liability risk
drivers and the driver selector of the system. Additionally, the
inventive system/method is capable of automated signal generation based
upon the expected loss in areas with insufficient historic loss
information and no tariffs. No other system known in the prior art is
able to achieve the explained objective in this way.

[0141] Another advantage is that the technical assembly and structure of
the system mirrors the outside world. It can easily be verified to
systematics and errors. The approach in the prior art systems is based
upon the investigation into solving the questions (i) What is the
expected loss compared to past loss experience? and (ii) How much premium
do I need to get? Though the method is self-adapting, the inventive
system is based on the questions: (i) What can go wrong?, (ii) How likely
is it to go wrong?, (iii) How much will it cost if something goes wrong?
Thus, the system becomes much more transparent. Through the ongoing
process of adaption, loss history is rather used to calibrate the system
parameters. In this way, the inventive system is also less vulnerable to
systematics and/or missing data. The system starts from a simple
structure and gradually extends it. The more data become available, the
more the system moves to finer granularity. In all process states, the
system stays modular and transparent. The system selects automatically
the right variables (meaning straightforward variables) at the right
place. This further improves the stability against errors and the
transparency. For example, the direct consequence of a loss is injured
people, damaged property, etc., rather than cost. By tracing the measure
parameters, the system chooses the right measure parameters. This is a
further big advantage over the systems known in the prior art.